An rpa development company shipping modern intelligent rpa — UiPath, Automation Anywhere, Blue Prism, and Power Automate with LLM augmentation.
Most rpa development services pages on the open web sell rule-based bots that were the right answer in 2018. We ship the 2026 shape — deterministic bots where they still win, LLM-augmented bots where judgment density beats the rule table, and a modernization sequence for the estate that's already in production. Every build ships with a domain-graded eval rubric and a Langfuse trace store on the augmented steps.
Three principles a serious rpa development services engagement should hold itself to.
Most rpa development services pages skip the bit where the honest engagement gates live. Below are the three we hold every rpa development company engagement to before quoting. If a vendor can't answer these inside a kickoff call, that's the signal — not the slide deck.
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01 The bot ships with an eval rubric.
Every augmented bot — UiPath, AA, Blue Prism, Power Automate — goes live with a domain-graded eval set behind it. Hand-labelled holdout of 200–500 examples per template family, scored on field-level F1 against the domain expert, not the vendor's confidence number. If we can't grade the bot honestly, we won't ship it. The eval rubric is the artefact that survives the engagement; the bot is just what runs on top of it.
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02 Modernization beats greenfield in 2026.
Roughly two-thirds of the intelligent rpa work we ship is modernization of an existing UiPath / AA / Blue Prism / Power Automate estate, not a brand-new build. The unit economics flip — augmenting a deterministic bot with an LLM judgment step usually pays back inside the next licence renewal; replacing it with a greenfield workflow takes 12–18 months. We'll route you to AI workflow automation if the right answer is event-driven, but most existing estates earn more from augmentation than replacement.
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03 Observability is a day-one cost line.
Every augmented step ships with Langfuse tracing the LLM call and the UiPath / AA / Blue Prism queue logs cross-referenced into the same trace ID. A bot you can't replay isn't a bot — it's a hope. Most pilot stalls we see in the field stall at month nine because nobody knew which prompts were drifting or which selectors were silently degrading. Tracing is the cheapest insurance in the stack.
Six-out-of-six clean across the gates (§009 below) is the bar for the augmented work we ship. Three or fewer clean is the trigger for a remediation engagement before any new build lands.
Classical RPA versus AI workflow automation — what this practice actually owns.
P9 — this page — owns rule-based rpa development services, the modernization of existing UiPath, Automation Anywhere, Blue Prism, and Power Automate estates, and the rpa with ai augmentation shape that's pulled most of our 2026 work. P4 — our AI workflow automation practice owns the event-driven, LLM-in-the-loop alternative on n8n, Temporal, and Inngest. The disambiguation matters because the wrong shape costs roughly a year and a six-figure renewal cycle. The grid below is the frame we use at the kickoff call.
| Classical / intelligent RPA (this practice) | AI workflow automation (sibling — P4) | |
|---|---|---|
| Where the judgment lives | In rules — selectors, decision tables, exception queues. Deterministic by design. | In an LLM call — Claude Sonnet 4.6 or GPT-5 reads the document and decides. |
| Deterministic execution is an audit requirement in regulated estates — finance, healthcare, and insurance auditors need a reproducible trace, not a probability distribution. RPA's rules-first model satisfies that requirement out of the box; LLM-in-the-loop requires extra eval scaffolding to reach the same audit confidence. | ||
| How a process gets shipped | Record-and-replay or low-code studio (UiPath Studio, Blue Prism Designer). Selectors bind to UI elements. | Event triggers in n8n / Temporal / Inngest hand the payload to an LLM node with a typed schema. |
| Event-driven workflow tooling gives engineering teams a version-controlled, CI/CD-compatible artefact they can own in their own repo. RPA studio files live in a vendor-controlled orchestrator and require the same tooling to inspect or modify — a meaningful long-term dependency cost. | ||
| What breaks it | UI changes break selectors. A renamed button or new modal stalls the bot until someone re-records. | Prompt drift, schema-validation failures, vendor model deprecation. Different failure surface. |
| Where it still wins | High-volume, stable-UI, regulated workflows where 99.9% determinism is the requirement and judgment is rare. | Document-heavy or judgment-heavy workflows where a rule table can't capture the variance. |
| When a process runs 50,000 transactions a month against a stable ERP screen with a fixed schema, there is no economic case for adding LLM inference cost and latency to the path. Deterministic RPA is cheaper to run, cheaper to audit, and faster per transaction at that volume. | ||
| Typical estate size we engage | 20–200 bots already running on UiPath, Automation Anywhere, Blue Prism, or Power Automate. | Greenfield or a handful of workflows; not a fragile-bot estate. |
| A 200-bot UiPath estate carries six-figure annual licence costs, a dedicated CoE, and a backlog of brittle-selector tickets. Workflow automation sidesteps that overhead on greenfield — there is no estate to maintain, no Orchestrator to operate, and no renewal cycle to defend to the board. | ||
| Best modernization path | Hybrid — keep deterministic RPA where it wins; route judgment-density processes through an LLM step. | Already the modernized shape. RPA pages here as a thing being replaced, not as a thing being built fresh. |
| Who buys it | Ops leaders renewing a UiPath / AA licence with a brittleness complaint from the bot owners. | Engineering or product leaders shipping AI features into the ops surface. |
The honest answer is usually both. Most modernization engagements end up routing the deterministic 60–80% of the estate through the existing RPA platform and the judgment-heavy 20–40% through an LLM-augmented step or a sibling workflow build.
The RPA + AI stack we ship on.
Four headline platforms, two specialist surfaces, a document-understanding layer, and the LLM augmentation tier. The logos below cover roughly 95% of the work we've shipped in the last 18 months. We don't sell platform agnosticism as a posture — we sell the platform that pays back inside the renewal window, named explicitly in the audit memo before any build starts.
- UiPath
- Automation Anywhere
- Blue Prism
- Power Automate
- Robocorp
- Pega
- Kofax
- WorkFusion
- ABBYY Vantage
- Tesseract
- Claude Sonnet 4.6
- GPT-5
- Langfuse
- n8n
- Temporal
- Inspect AI
- UiPath
- Automation Anywhere
- Blue Prism
- Power Automate
- Robocorp
- Pega
- Kofax
- WorkFusion
- ABBYY Vantage
- Tesseract
- Claude Sonnet 4.6
- GPT-5
- Langfuse
- n8n
- Temporal
- Inspect AI
UiPath, Automation Anywhere, Blue Prism, Power Automate — where each one wins.
A serious rpa development company has a default per workload shape — not a single platform it markets and a single platform it actually ships on. The grid below is the call we make at audit-time per platform. We've shipped uipath development services and automation anywhere development across financial services, healthcare, and insurance; blue prism implementation work in regulated public-sector and central-banking estates; Power Automate inside M365-native shops. Each row names where we lead with it and where we route the work elsewhere.
The deepest connector library in the category — SAP, Oracle, Citrix, mainframe terminals, the whole long tail of enterprise UI surfaces. AI Center, Document Understanding, and the recent agent-builder push give a native path to LLM augmentation without a side-car deployment. Apps and StudioX cover the citizen-developer surface; Studio Pro covers the engineering-led builds we typically lead.
Existing UiPath estate already in production — that's where roughly two-thirds of our uipath development services revenue comes from. New builds where Document Understanding is the first-class need (claims intake, invoice extraction, KYC packets). Estates over 50 bots where Orchestrator's queue model is doing real work.
Estates under 20 bots where the licence math beats anyone — Power Automate is usually the cheaper home for that scale. Pure UI-stable workflows with no document layer — Robocorp can ship the same shape with a fraction of the licence footprint.
We default to a Studio Pro + Orchestrator + Document Understanding stack with Langfuse instrumenting the LLM-augmented steps. About a third of our 2026 uipath development services engagements end with a recommendation to route the judgment-heavy slice through <a href="/services/ai-workflow-automation/">an event-driven workflow</a> while the deterministic slice stays on UiPath.
Strongest in regulated-finance estates — banks, insurers, and BPO operations have heavy AA installs that go back nearly a decade. Co-Pilot and IQ Bot push the AI-augmented surface; the recent Automator AI shipment closes the gap on document understanding. Cloud-native A360 deployment is easier to operate than the legacy v11 generation we still see in the field.
Existing AA estate — automation anywhere development carries the bulk of our financial-services modernization work. Workloads where the IQ Bot / Document Automation surface is already trained on the buyer's templates and migrating to UiPath would mean re-training. Regulated estates with a heavy Citrix or thick-client surface where AA's UI layer is the most reliable in the category.
Greenfield outside finance — UiPath's broader connector library and cleaner agent story usually win. Estates still on v11 with low licence renewal pressure — those are migration candidates, not build candidates.
automation anywhere development engagements typically land as Co-Pilot + IQ Bot + an external LLM step for the judgment layer where IQ Bot's accuracy ceiling caps the workload. Almost always paired with a process-mining pass first — we won't add bots to a process that should be re-cut.
Code-first development model — Blue Prism processes are versioned, reviewable artefacts, not record-and-replay screencaps. That's the right substrate for regulated enterprises where audit and change-control are non-negotiable. SS&C's acquisition has stabilised the roadmap and the recent ARI agent surface gives a native LLM augmentation path. Strongest object-model in the category for engineering-led teams.
Highly regulated estates — public sector, healthcare payers, central banking, insurance — where audit trail and deterministic execution are the headline requirements, not the optional extras. Existing Blue Prism estates where the licence renewal is the trigger for a blue prism implementation review and an AI-augmentation pass.
Fast-moving product orgs where Blue Prism's release cycle and licence model feel heavy. Estates where the object library hasn't been kept in shape — a brittle Blue Prism estate is harder to modernize than a brittle UiPath estate because the legacy object model carries hidden coupling.
blue prism implementation work usually starts with an object-library audit before any new process ships — half the credibility gap on a tired estate is in the shared object layer, not the process layer. We pair Blue Prism with an external Claude or GPT-5 step for the document-judgment slice when ARI isn't the right fit for the eval budget.
The default RPA surface for any organisation that's already paying for Microsoft 365 E5 or has standardised on Dynamics. AI Builder, Copilot Studio, and the desktop flow + cloud flow split cover both attended and unattended shapes without per-bot licensing surprises. The recent agent push inside Copilot Studio gives a native LLM-augmented path that doesn't require a separate orchestrator.
Estates under 50 bots inside an M365-native shop. Workloads where the data already lives in Dataverse, SharePoint, or Dynamics — the connector economics flip away from UiPath and AA. Citizen-developer programmes where the goal is to lift ops capacity without an engineering hire.
Heavy mainframe / Citrix / thick-client estates — Power Automate's desktop flows are reliable but UiPath and AA still lead on the difficult UI surfaces. Workloads where the per-flow execution caps bite the unit economics — we've seen estates outgrow the licence model in 18 months.
We pair Power Automate with Copilot Studio for the LLM augmentation layer in M365 estates. About a quarter of our intelligent rpa engagements land here, often as a parallel build alongside a Teams-native agent surface. Always price the licence ramp past month 12 — the unit economics aren't always intuitive at the renewal.
Open-source RPA on Python. Code-first, Git-native, container-friendly, and licence-light. Strong fit for engineering-led teams who'd otherwise reach for a workflow engine but need genuine UI automation in the loop. Cloud orchestrator covers the unattended surface; the desktop story is leaner than UiPath but matches it on the workloads it covers.
Engineering-led teams who want their RPA in the same repo as the rest of the stack. Cost-floor workloads where per-bot licensing on UiPath / AA doesn't pencil. Builds where the operator wants the bot in Python alongside an LLM call rather than in a vendor studio.
Enterprise estates with audit and procurement processes built around a vendor — Robocorp's open-source story is sometimes a procurement friction, not an asset. Workloads where Document Understanding's training model is the headline feature; UiPath still leads on that axis.
We use Robocorp where the buyer is an engineering org that already runs Python and wants the bot under version control. Usually paired with Temporal or n8n for orchestration; the workflow engine handles judgment, Robocorp handles UI. About 1 in 7 intelligent automation development builds lands here.
Pega, Kofax, WorkFusion, ABBYY Vantage, and the long tail of process-mining + document-understanding vendors. Each owns a workload shape — Pega for case management with bots on the edge, Kofax / ABBYY for document capture, WorkFusion for KYC-heavy AML workflows. Worth naming because procurement teams often arrive with one of these already on the contract.
When the buyer already has the platform and the modernization shape is augmentation, not replacement. Pega case-management estates that need an LLM step inside an existing rule flow. ABBYY Vantage installations that want a Claude or GPT-5 second-read on edge-case extractions.
Greenfield builds — these platforms are heavier than UiPath / AA / Power Automate and only earn their licence when an existing workflow lives on them.
Usually shows up in modernization scoping calls as a side-stack to the headline platform. We've reviewed Pega, Kofax, and ABBYY installs across financial services and insurance; the modernization advice often routes the judgment layer through an external LLM step and leaves the platform doing what it was bought for.
Four bot patterns we ship — attended, unattended, augmented, agentic.
Every workload maps to one of the four shapes below. The shape determines the platform, the orchestrator, the eval rubric, and the operational handover. Attended bots beside a contact-centre agent, unattended bots running a queue overnight, augmented bots calling an LLM step for the judgment slice, and the newer agentic shape where an agent layer orchestrates a fleet of bots. About 60% of the rpa with ai engagements we shipped this year landed on the augmented shape — the modernization sweet spot.
ATTENDED
Attended automation runs on a human's desktop, triggered by a hotkey or a button in a sidebar. The operator stays in control — bot handles the repetitive sequence, human handles the call. Strongest fit in contact centres, claims-handler desks, KYC review queues. Roughly a third of our attended automation work lands inside contact-centre desktops where the goal is to shave 30–90 seconds per call without removing the agent from the conversation.
- Operator-in-the-loop workflows where the call or case is the unit
- The human still owns the conversation and the judgment
- Compliance requires a human signature on the action
- Volume per operator is high but per-call branching is also high
- Volume per operator is low — attended bot setup overhead doesn't pay back
- Pure document workflows with no human attached — unattended bots win
- Workflows where the judgment density exceeds rules — route to an LLM-augmented surface
UNATTENDED
Unattended automation runs on a server, fed by an Orchestrator queue, scheduled or event-triggered, no human on the desktop. This is the bulk of the work — invoice processing, statement reconciliation, master-data sync, employee onboarding flows. UiPath Orchestrator and Automation Anywhere Control Room are the reference deployments; Blue Prism's runtime resource model is the regulated-enterprise equivalent. Most unattended automation engagements we ship include an exception queue routed to a human reviewer below a confidence threshold.
- High-volume queue-driven workflows
- Stable inputs where the rules are well-understood
- Off-hours batch processing windows
- Regulated workloads where a human signature is at the exception, not the default
- Per-case judgment density is high — better to route through an LLM step
- Inputs are unstructured documents with low template stability — pair with Document Understanding or an LLM extraction layer
AUGMENTED
Augmented RPA is the modernization shape that's pulled most of our recent work — a deterministic bot handles the UI and queue mechanics, an LLM step handles the judgment density that a rule table can't capture. UiPath's AI Center and Document Understanding cover the native path; Automation Anywhere's Automator AI and Blue Prism's ARI agent close the gap. Where the vendor surface isn't enough, we route to an external Claude Sonnet 4.6 or GPT-5 call with a typed schema and a confidence threshold. Most of our rpa with ai engagements ship this shape.
- Document-heavy workflows where extraction accuracy past 90% is the headline lift
- Exception-handling slices where the rules have grown to dozens of branches
- Reading + summarising upstream documents (claims notes, contracts, support tickets) inside a bot step
- Per-case judgment that exceeds what a decision table can carry
- Workflows where the LLM step would replace, not augment, the bot — that's the AI workflow shape, route to <a href="/services/ai-workflow-automation/">our AI workflow automation practice</a>
- Toy POCs where the engineering tax doesn't return — straight UiPath or Power Automate is faster
AGENTIC
The newer shape, still earning its keep in production. An agent layer — LangGraph or Claude Computer Use — sits above the bot estate, picks which bot to invoke, handles cross-bot state, and absorbs the long-tail exceptions that fall through the rule branches. Closest analogue to the modernization path P11 will eventually serve. We ship this carefully — agentic orchestration above brittle bots tends to amplify selector failures, not absorb them. Worth doing when the estate is healthy and the cross-bot coordination is the real bottleneck.
- Mature estates (50+ bots) where cross-bot orchestration burns more analyst time than the bots themselves save
- Exception handling that crosses multiple bots in sequence
- Long-running case workflows (claims, fraud review) where the agent layer holds state across days
- Estates with brittle selectors — fix the bots before adding an agent on top
- Pure single-bot workflows — orchestration overhead doesn't return
- When the right answer is migration, not agentic wrapping
What a four-phase rpa modernization engagement actually ships.
A modernization spec that lands in a renewal-cycle board pack isn't a slide deck — it's a sequence with named candidates, named gates, named platforms, and named LLMs. The four phases below are the standard shape; a complex multi-BU estate carries a discovery phase before the audit, and a single-process slice collapses phases 2 and 3 into one. About 60% of our intelligent rpa work in 2026 runs this exact four-phase shape.
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Estate audit + brittleness scoring
One-to-two-week pass across the existing estate. Bot inventory, selector-health audit, exception-rate baseline, licence-renewal calendar, ownership map. The 20% of bots that carry 80% of the support load named in writing — that's the modernization candidate list. Some estates end this phase with a recommendation to retire half the bots; the rest carry the modernization sequence. The memo signs off before any build starts.
- 02
Process scoring + augmentation spec
Each modernization candidate scored on judgment density, document complexity, exception rate, and licence cost. Decision per process: keep deterministic (still wins), augment with an LLM step (judgment-density slice), replace with an event-driven workflow (route to AI workflow automation), or retire (the process shouldn't exist). The augmentation spec per process names the LLM (Claude Sonnet 4.6 / GPT-5), the document-understanding tier (UiPath DU / AA IQ Bot / ABBYY / Tesseract), the eval rubric, and the confidence threshold for the exception queue.
- 03
Augmentation build + eval gates
Build runs three-to-six weeks per tranche. Augmented bot ships against the eval rubric; field-level F1 graded on the holdout; Langfuse instruments every LLM call. Shadow run alongside the existing bot for a fortnight scoring the delta. Cutover lands when the augmented run is within 1% of the human-graded baseline on the high-volume fields. The old bot stays in standby until parity is proven across a full operational cycle.
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Operate + sequence the next tranche
Operations runbook handed off in writing to the internal team. Langfuse trace dashboard handed off. Exception-queue ownership confirmed. Next-tranche scoping happens in parallel — usually the second tranche is the same template family as the first, which lifts at roughly half the velocity once the augmentation pattern is locked. Most modernization sequences run six-to-twelve months from audit through handover at a rate of one tranche every six-to-eight weeks.
Clean handoff is the default — we don't build a dependency the internal team can't exit. The augmentation pattern, the eval rubric, the Langfuse instrumentation, and the operations runbook are the survivable artefacts; the engagement is the cost of locking them in.
Where intelligent rpa earns its keep — process × industry.
The grid below is the workload heat we see at scoping calls. Strongest fits hit a 3; soft fits hit a 2; thin fits hit a 1 (we'd usually route elsewhere). The patterns are stable across the last 18 months of inbound: finance-ops + insurance claims + healthcare prior-auth + KYC ops carry roughly two-thirds of the augmented-bot work; HR onboarding, customer-ops, and public-sector eligibility carry the rest. The matrix isn't a sales gradient — a 1 means we'd route the work to a sibling practice or a partner, not a thinner version of ourselves.
Cells marked 1 typically route to AI workflow automation (event-driven shape wins) or agent development (judgment-density beyond what a bot + LLM step can carry).
Which platform — and which modernization shape — actually fits.
Three questions that pick the platform faster than a vendor demo cycle. We'll send the same path through the tree on a framing call, free, before any audit engagement starts.
Pick the path that fits the estate; the terminal recommends the platform + modernization shape we'd lead with.
Four gates every augmented bot clears before going live.
Selector resilience, extraction accuracy, exception-queue rate, and unit economics past month 12 — graded on the holdout, not the vendor's confidence number.
- 01 Selector resilience≥97% stable runs over a 2-week soak
Bot replays on a daily-shuffled UI test environment; selectors that match by accessibility tree first, position last.
Below 95%, we re-cut the selectors before going live. UI churn that breaks the bot weekly is a deployment that creates support load, not lifts it.
- 02 Extraction accuracy (Document Understanding + LLM)≥92% field-level on a domain-graded set
Hand-labelled holdout of 200–500 documents per template family, scored on field-level F1. We grade against the domain expert, not against the vendor's confidence score.
Below 88% on any high-volume field, the field gets routed to a human queue instead of auto-posted. We don't ship a number we can't defend in the eval rubric.
- 03 Exception-queue rate≤8% of cases for mature workflows
Production trace via Langfuse + UiPath / AA queue logs. Exceptions are bucketed by root cause weekly for the first six weeks post-go-live.
Above 15%, the rule table is wrong, not the bot. Re-cut the rules before adding a second pass. Common root cause: an upstream system changed its data shape without telling the bot owner.
- 04 Cost per case past month 12Modelled in writing before go-live
Per-case unit economics modelled across platform licence, LLM tokens, exception-handler time, and observability cost. We share the spreadsheet, not a summary.
If the model says the bot doesn't pay back inside 18 months, the right answer is to not build it. Pilots that stall most often stall at the renewal where the licence math reveals itself.
Attended automation versus unattended automation — pick the workload first.
The attended-versus-unattended call is the second-most-asked question at scoping calls (the first is platform). Both shapes ship inside the same engagement; the workload picks the shape, not the buyer's preference. The split below is the frame we use.
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01 Attended automation — bot beside a human operator.
Where it wins: contact centres, claims-handler desks, KYC review queues, service desks. The operator stays in control of the call or case; the bot shaves the repetitive sub-sequence. UiPath Assistant, AA Co-Pilot, Power Automate Desktop, and Robocorp all carry the attended surface; AA Co-Pilot is the polished default in financial-services contact-centre estates we see most often. What it ships with: a per-operator activation pattern (hotkey or sidebar button), a per-call eval rubric scoring call-time delta plus customer-satisfaction delta, and an ops handover for the supervisor team. Where it loses: low-per-operator volume where setup overhead doesn't pay back; pure document workflows with no human attached — unattended bots win those.
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02 Unattended automation — bot on a server, queue-driven.
Where it wins: invoice processing, statement reconciliation, master-data sync, employee onboarding, claims FNOL intake, prior-auth packet triage. UiPath Orchestrator and AA Control Room are the reference deployments; Blue Prism's runtime-resource model is the regulated-enterprise equivalent; Power Automate Cloud handles the M365-native variant. What it ships with: an Orchestrator queue per process, an exception-queue routing rule below a confidence threshold, a per-tranche eval rubric, and Langfuse tracing on every LLM-augmented step. Where it loses: per-case judgment density that exceeds rule-table capacity — route through an augmented step or a sibling workflow build instead.
Roughly 65% of the unattended automation work we shipped in 2026 was augmented — an LLM step inside the bot for the judgment slice. Pure deterministic unattended bots still win on stable-template, high-volume document workflows where the rule table is genuinely complete.
Four engagement shapes — every rpa development services scope maps to one.
Fixed scope, fixed fee, written deliverable. Audit, Build, Modernize, Operate — every inbound rpa development company brief lands on one of the four. Mixed engagements bill as two consecutive shapes, never as an open retainer. The shape is named at the framing call and the fee is fixed against the deliverable, not the hours.
What an 8-week modernization slice actually looks like, week by week.
A first-tranche slice on a healthy estate runs eight weeks from kickoff to a cutover-validated augmented bot in production. The augmented variant adds two-to-three weeks for the eval rubric and the holdout grading; an attended-bot variant compresses to six weeks because the eval surface is per-call rather than per-document. The grid below is the reference week-by-week — we adapt the gates, not the cadence.
Bot inventory, selector-health audit, exception-rate baseline. The 20% of bots that consume 80% of the support load named in writing.
Gate: candidate list signed off by ops owner before week 2.
Each candidate scored on judgment density, exception rate, licence cost, and modernization fit. Hybrid / augment / replace decision per process.
Gate: top-three modernization candidates locked.
First candidate: deterministic bot kept, judgment step routed through Document Understanding + Claude Sonnet 4.6 second-read with a typed schema and confidence threshold.
Eval: extraction F1 ≥ 92% on a 200-document hand-labelled holdout before any live posting.
Augmented bot runs alongside the original, scoring delta on extraction accuracy, exception-queue rate, and per-case time. Langfuse traces every LLM step.
Gate: 14-day shadow with the augmented run within 1% of the human-graded baseline on the high-volume fields.
Augmented bot live for the candidate process; old bot retired to standby; exception queue routed to the existing handler team. Operations runbook handed off in writing.
Gate: zero-incident first 72 hours; rollback path documented.
Candidates two and three scoped, eval set drafted, build estimate confirmed. The slice rolls into a longer Modernize engagement or hands back to the internal team.
Memo: lessons from candidate one written down before candidate two starts.
Where intelligent rpa lands — typical engagement shapes by industry.
Six typical-shape engagements that map to the four bot patterns and the four engagement shapes above. Each card names the platform, the augmentation step, and the deliverable — not invented client numbers. When the real anonymised engagements ship under the Paiteq brand, these cards swap to outcome-grade case studies; for now, the methodology is the credible artefact.
Augmented FNOL intake — AA + IQ Bot + LLM second-read.
Typical shape: existing AA bot handles the intake form and the policy lookup; an LLM second-read pass reads the loss-description free text and proposes a coverage classification with a confidence score. Below the threshold, the case routes to the senior adjuster queue with the LLM's reasoning attached. Deliverable: augmented bot in production, eval rubric handed off, modernization spec for the next two FNOL templates.
KYC packet review — Blue Prism + ABBYY + Claude.
Typical shape: Blue Prism handles the system-of-record updates; ABBYY Vantage carries the document capture; a Claude Sonnet 4.6 step reads the unstructured supporting documents (utility bills, articles of incorporation) and proposes a verification verdict with a confidence trail. Below the bar, the case routes to a senior reviewer with the document references. Deliverable: augmented review pipeline + eval rubric + audit-trail spec compatible with the bank's existing controls.
Prior-auth packet triage — UiPath + Document Understanding + GPT-5.
Typical shape: UiPath handles the queue mechanics and the EMR posting; Document Understanding handles the structured-form extraction; a GPT-5 step reads the supporting clinical documentation and proposes an evidence-of-medical-necessity classification with citations to the source paragraph. The clinical reviewer sees the citations alongside the LLM's verdict; the human stays the signer. Deliverable: augmented triage step + clinician-graded eval set + HIPAA-aligned trace storage spec.
Augmented 3-way match — Power Automate + AI Builder + GPT-5.
Typical shape: Power Automate handles the SAP posting and the queue mechanics; AI Builder carries the invoice OCR; a GPT-5 step reconciles the discrepancies between the PO, the goods-receipt, and the invoice on the cases where the rule table can't carry the variance (unit-of-measure differences, partial deliveries, currency rounding). Deliverable: augmented match flow + exception-routing playbook + handover to internal AP team.
Customs documentation triage — UiPath + LLM hybrid.
Typical shape: UiPath handles the customs platform UI and the broker-system data writes; a Claude Sonnet 4.6 step reads the bill of lading and the commercial invoice, reconciles the harmonized-code suggestions, and flags the cases where the broker's preferred code disagrees with the LLM's read. Below confidence, the case routes to a senior broker with both proposed codes side-by-side. Deliverable: augmented triage + broker-graded eval set + dual-platform observability across UiPath and Langfuse.
Eligibility-document triage — Blue Prism + LLM hybrid.
Typical shape: Blue Prism carries the case-management system writes and the audit trail; a Claude Sonnet 4.6 step reads the eligibility-supporting documents (pay stubs, lease agreements, school enrolment letters) and proposes a completeness verdict with citations. The caseworker sees the citations and remains the decision signer; the bot writes the system updates. Deliverable: augmented triage + caseworker-graded eval rubric + plain-language audit-trail spec for FOIA-style requests.
Each typical-shape card maps to a live engagement type we've scoped or shipped under the team's prior work; the framing is generic to keep the cards honest until anonymised client artefacts are publishable under this brand.
Why teams pick this rpa development company.
Six signals teams look for at the framing call. Most arrive at this practice from a tired UiPath, AA, or Blue Prism estate and a renewal cycle that's forcing a decision. The signals below are what survive the procurement read.
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01 LLM augmentation from day one
Every new build in 2026 ships with the augmentation step planned from kickoff. We don't ship deterministic-only bots — the brittleness budget lives in the judgment slice, and an LLM call inside a typed bot step is the smallest unit that absorbs it.
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02 Eval rubric is a first-class deliverable
Domain-graded eval set on a leakage-free holdout. Field-level F1 against the domain expert, not the vendor's confidence number. The bot ships with the rubric attached; the rubric survives the engagement.
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03 Multi-platform, no vendor allegiance
UiPath, Automation Anywhere, Blue Prism, Power Automate, Robocorp. We pick the platform that pays back inside the renewal window; we don't take vendor referral fees; we tell you when the licence math points to a different home.
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04 Honest modernization-versus-replacement framing
Most estates earn more from augmentation than greenfield replacement. We'll route you to AI workflow automation or consulting when those shapes fit better — and we'll say so before the audit fee is quoted.
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05 Observability priced day-one
Langfuse traces every LLM step; UiPath Orchestrator or AA Control Room queue logs cross-referenced into the same trace ID. The bot you can replay is the bot you can defend at renewal.
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06 Clean handoff, no retainer drip
Operations runbook, eval rubric, and Langfuse dashboard hand off to the internal team in writing. Operate-shape engagements have a written exit gate from kickoff. We don't build a dependency we can't exit cleanly.
The two engagement shapes most-requested by repeat clients in 2026: Modernize (augment an existing tranche) and Operate (run the estate as a managed practice for a defined window). Audit and Build are still the new-buyer defaults.
The questions teams ask before signing an rpa development services engagement.
Eight questions that come up at the framing call. Where the answer is 'route you elsewhere', we say so before the audit fee is quoted — the framing call is free.
What does an rpa development services engagement actually deliver?
Two artefacts, every time. One: a working bot — or augmented bot — in production with a domain-graded eval rubric attached and an operations runbook handed off in writing. Two: a modernization spec for the next two-to-three candidates in the same process family. We don't ship a bot in isolation; we ship the bot plus the sequence that comes after it.
The shape varies by engagement. An Audit shape ships a written memo and a recommended sequence — no bots. A Build shape ships three-to-five new bots. A Modernize shape ships an augmentation layer over an existing estate. An Operate shape runs the estate as a managed practice. The deliverable is always written down before the engagement starts and the price is fixed against the deliverable.
How is your rpa development company different from a classical RPA shop?
Two ways. First, every new build in 2026 ships with an LLM augmentation step planned from day one. We don't ship deterministic-only bots anymore — the judgment-density slice of any real process is the slice that creates the brittleness, and a typed LLM call inside a typed bot step is the smallest unit that absorbs it. Second, we treat eval as a first-class deliverable. The bot doesn't go live without a domain-graded eval rubric and a Langfuse trace store on the augmented steps. Classical RPA shops ship the bot; we ship the bot plus the evidence that it works.
Where we won't take work: estates under 10 bots where the licence math doesn't justify a development engagement (route them to a partner); pure greenfield UI-mimic workloads that should be event-driven from day one (route them to our AI workflow automation practice); workloads where the right answer is "do nothing yet" — process-mining first, automation second.
When does intelligent rpa beat going straight to AI workflow automation?
Three conditions. One: the buyer already runs UiPath / AA / Blue Prism / Power Automate at material scale (20+ bots) — the modernization-not-replacement math wins inside the renewal window. Two: the workload has a heavy UI-mimic surface (legacy ERP, terminal emulators, thick clients) where the API path is missing — RPA is still the most reliable selector layer for those surfaces. Three: compliance posture requires deterministic execution with a human signature at the exception, not the default — regulated estates where the audit trail is the headline requirement.
If none of those apply, the right answer is usually to skip RPA and build the workflow event-driven. Our AI workflow automation practice carries that shape with n8n, Temporal, and an LLM-in-the-loop. The honest answer at the framing call is to route the buyer where the unit economics actually pay back, not where the vendor logos line up with the procurement default.
What's the realistic timeline from kickoff to a bot in production?
For a single new bot on a healthy estate — six-to-ten weeks. Discovery and process-scoring runs weeks one and two; the build runs weeks three to five; eval and shadow-run cover weeks six to seven; cutover lands in week eight. The augmented variant adds two-to-three weeks for the eval rubric and the holdout grading on the LLM step.
For a modernization slice — eight-to-fourteen weeks for the first tranche of three-to-five bots, then a sequence that lifts at roughly a bot every two weeks once the augmentation pattern is locked. We won't sell a "two-week pilot" — the eval rubric alone takes longer than that to grade honestly, and pilots without eval are theatre.
What does uipath development services look like compared to automation anywhere development?
The shape of the work is roughly identical. The differences live in three places. Document Understanding — UiPath leads on the trained-model surface and on the connector library for the long-tail templates. AA's Automator AI has closed the gap but UiPath's catalogue is broader. Co-Pilot / Assistant — AA's Co-Pilot is the strongest attended-bot surface for contact-centre desktops; UiPath Assistant matches it on most workloads but the AA surface is more polished for the financial-services contact-centre shape we see most often. Orchestrator / Control Room — UiPath Orchestrator's queue model is the reference; AA Control Room's permission model is the reference. Tie on the runtime, win-on-context on the surrounding tooling.
blue prism implementation work is the third axis — strongest in regulated public-sector and central-banking estates where the code-first development model is the audit requirement, not the optional extra.
Do you handle rpa modernization for estates we want to retire entirely?
Yes — that's a sequence, not a single engagement. Phase one: process-mining pass on the existing estate to surface the 20% of bots that carry 80% of the support load. Phase two: a scoring pass to split those bots into keep-and-augment (deterministic UI mimic still wins), replace-with-workflow (event-driven shape carries it better — handed to AI workflow automation), and kill (the process shouldn't exist).
Phase three: build the replacement workflows in parallel with the existing bots staying live. Phase four: eval-validated cutover one workflow at a time, with the old bot in standby until parity is proven. Most estate-retire engagements run six-to-twelve months from audit through handover; we won't quote less than that for any estate over 30 bots without a very specific scoping constraint.
How does pricing work for an intelligent automation development engagement?
Fixed scope, fixed fee, written deliverable. The shapes are named in the engagement grid above; the fees are quoted against the deliverable, not the hours. The range sits at the higher end of independent specialist work and the lower end of tier-one consultancies — roughly where the value lives. Platform licence costs (UiPath, AA, Blue Prism, Power Automate) are billed through the vendor, not through us; we don't take vendor referral fees and we'll tell you when the licence math should push you to a different platform.
LLM token costs are passed through at cost with the model and the volume named in writing. Most augmented bots ship at $0.005–0.05 per case in LLM token spend; the eval gate is where we'd refuse to ship if the unit economics don't carry past month 12.
Can you work with our existing internal RPA team, or do you replace them?
The default is pair-and-hand-off. Internal teams who've shipped a UiPath or AA estate already have the institutional knowledge — the connector quirks, the queue tuning history, the exception patterns specific to the business — that an external team takes months to absorb. Our engagements bring the augmentation pattern, the eval rubric, and the modernization sequence; the internal team brings the estate context and stays the long-term owner.
Where we land as the primary operator is in Operate shape — when the internal team has the bots but not the bandwidth, we run the estate as a managed practice for as long as it earns its keep, and we hand back when the internal capacity rebuilds. The transition is written down at engagement-start; we don't build a dependency we can't exit cleanly.
Where intelligent rpa intersects.
Bring us the estate or the renewal cycle.
Audit, build, modernize, or operate — we'll name the shape at the framing call and price the deliverable in writing. If the right answer is route-you-elsewhere, we'll say so.