Sales & GTM
Lead qualification agents, outbound personalization at scale, CRM hygiene, transcript-to-CRM, account research.
AI automation agency · live AI automation agency.
AI automation agency.
Workflows that actually ship.
AI automation services and AI workflow automation for businesses that need outcomes, not pilot projects. We design, build, and run AI automation solutions end-to-end: agents, RAG, document workflows, sales and support automation. Workflows go live in 6–8 weeks. Cost-of-ownership reported monthly, per workflow.
Definition
What is AI workflow automation?
AI workflow automation is the practice of connecting LLM reasoning to existing business systems (CRMs, ERPs, ticketing tools, ops platforms) so that decisions and actions previously needing a human happen automatically under defined guardrails. Common workflows include invoice triage, refund approvals under a dollar threshold, tier-1 ticket resolution, and contract clause review. Unlike robotic process automation (UiPath, Automation Anywhere) which scripts deterministic UI clicks against legacy applications, AI automation uses language models to interpret unstructured input and pick the right next action. Unlike a basic Zapier flow, AI automation includes confidence-gated escalation, idempotent webhook handlers, and reconcile-on-failure logic. Common stacks pair n8n, Temporal, or LangGraph with Claude Sonnet 4.6 or GPT-5 and Langfuse for observability.
6–8 wk
kickoff to first workflow live in production
Monthly
cost-of-ownership reported per workflow
Model-agnostic
Claude · GPT-5 · open per use case
Your repo
code, prompts, and data stay with you
how the loop actually runs Agents that do the work,
Agents that do the work,
not just describe it.
Every workflow we ship is a tight loop: reason, call a tool, observe, repeat. Eval at every hop. Guardrails between every call.
invoice-processing-agent · trace
thought Email arrived from vendor — need to extract PO + post to ERP.
action memory.recall(vendor.payment_terms)
obs → NET-30, auto-approve under $5K, requires PO match
action db.query(po where vendor=#V8821, open=true)
obs → PO #8821 found · $4,212 · 4 line items
thought Amounts match. Confidence 0.94. Under threshold.
action do.action(NetSuite.createBill po=#8821)
obs → ok · bill #B-3719 posted · $0.003 cost · 14 min saved
reply Invoice posted to NetSuite. AP analyst notified.
step 1/9 · 5 tools · 0 guard-trips
01
Eval-first
We write evals before we write the agent. Pass-rate, not vibes.
02
Guarded tools
Every tool call goes through a policy layer. No surprises.
03
Observable
Every step traced. Every regression caught before users see it.
04
Cost-aware
Routing across Haiku, Sonnet, Opus. Right model, right step.
workflows we automate Six AI automation services
Six AI automation services
that pay back fast.
These are the AI automation services we ship most often: sales automation, document workflows, support deflection, internal copilots, reporting agents. Every AI workflow automation engagement is ranked in the audit by ROI, risk, and time-to-ship; usually the highest-ROI candidate surprises everyone.
Operations
Invoice and PO processing, exception triage, vendor onboarding, contract intake, document routing.
Customer support
Tier-1 deflection with RAG over your docs, ticket triage and routing, multilingual reply drafts, escalation summarization.
Internal tools
Meeting summarization + action capture, internal knowledge agents, Slack copilots, weekly digest generation.
Document workflows
Contract review and clause extraction, claim adjudication assistance, policy compliance checks, redaction.
Reporting & analytics
Weekly business digests, anomaly detection on KPIs, natural-language reporting, board-pack assembly.
Don't see your workflow?
The highest-ROI workflow on your team is usually one we haven't listed. Bring it to the two-week audit — we'll rank it against the rest and tell you if it ships.
Tell us yours ai workflow automation in 6–8 weeks How we ship AI workflow automation
How we ship AI workflow automation
without the consulting-deck detour.
Four steps, milestone-billed, with explicit kill points. Every AI workflow automation engagement runs this same loop: discover, pilot, ship, scale. If the metric doesn't move at the pilot stage, we'll tell you and you walk away. No retainer trap.
- Weeks 1–2
Discover
Two-week workflow audit. We sit with two or three operators, watch the actual work, and rank candidate workflows by ROI, risk, and time-to-ship.
90-day automation roadmap - Weeks 3–6
Pilot
Build on your single highest-ROI candidate. We integrate against real systems, deploy behind a feature flag, and measure baseline vs. assisted runs.
Live workflow behind a flag, with eval dataWalk-away point - Weeks 7–8
Ship
Production hardening. Logging, retry policy, fallbacks, eval suite, and a runbook. The workflow goes live with your team, not as a demo.
Production system + runbook - Ongoing
Scale
Move to the next workflow on the roadmap. Many clients run three to five workflows by month six. Same team, shared tooling, compounding learning.
3–5 workflows live by month six
the stack Tools we use, vendors we don't marry.
Tools we use, vendors we don't marry.
Right model for the workflow.
We pick per workflow. Some weeks Claude wins on long-context reasoning, some weeks Llama wins on cost. Our ai workflow automation services and agentic workflow automation builds share one portable layer (your workflow code and your eval suite). Everything else is replaceable. The same stack underpins our intelligent automation services for regulated buyers.
Anthropic Claude OpenAI GPT-5 Google Gemini Meta Llama Mistral Cohere Command Groq Together AI Anthropic Claude OpenAI GPT-5 Google Gemini Meta Llama Mistral Cohere Command Groq Together AI
LangChain LangGraph CrewAI LlamaIndex OpenAI Agents SDK DSPy Pydantic AI Vercel AI SDK LangChain LangGraph CrewAI LlamaIndex OpenAI Agents SDK DSPy Pydantic AI Vercel AI SDK
Pinecone pgvector Weaviate Qdrant n8n Temporal Modal Langfuse Pinecone pgvector Weaviate Qdrant n8n Temporal Modal Langfuse
what we actually ship Workflows defined in code.
Workflows defined in code.
Not in a no-code canvas.
Every workflow lives in your repo: versioned, reviewable, debuggable. The LLM swap is one variable. Try it: pick a model on the left, watch the code on the right.
38 lines of code
$0.004 per ticket · Claude
87% confidence routing
from getwidget.agents import LangGraphAgent, tool
from anthropic import Anthropic
claude = Anthropic()
@tool
def search_docs(query: str) -> list[dict]:
"""RAG over the customer's product docs."""
return vector_db.search(query, k=5)
@tool
def reply(ticket_id: int, body: str) -> dict:
return zendesk.update(
ticket_id, body=body, status="pending"
)
def triage(ticket: dict) -> dict:
agent = LangGraphAgent(
model="claude-sonnet-4-6",
tools=[search_docs, reply],
system=(
"Tier-1 support agent. "
"Draft a reply if confidence > 0.7, "
"else escalate."
),
max_steps=4,
log_to="langfuse",
)
return agent.run(ticket=ticket)
Real production workflow with the names changed. Lives in your repo.
engagement models Three ways to start.
Three ways to start.
Honest pricing, named outcomes.
We don't quote everything as a six-month engagement. Most clients start with an audit, ship one workflow on a pilot, then move to monthly for the next three to five.
2 weeks
Workflow audit
Find the workflows worth shipping before you commit a budget.
Fixed-fee fixed
- Two-day on-site or remote operator shadow
- ROI / risk / time-to-ship scoring
- 90-day automation roadmap (named workflows)
- Rough monthly cost-of-ownership per candidate
6–8 weeks
Pilot to production
One workflow, end-to-end, with eval data. Not a demo.
Fixed-bid fixed price
- Discovery + scoping on your highest-ROI candidate
- Build, integrate, deploy behind a feature flag
- Eval suite, logging, retry policy, fallback runbook
- Baseline vs. assisted metric report at end
- Explicit walk-away point: if the metric won't move, you don't pay phase 2
Monthly
Continuous automation team
Embedded squad shipping the next workflow on your roadmap.
monthly per month
- PM + AI engineer + ops analyst, embedded
- Monthly cost-of-ownership report per workflow
- Roadmap prioritization + new-workflow throughput
- Cancel any time. No annual contract
Your repo, your prompts Monthly cost report per workflow No annual contract Model-agnostic
capability patterns Workflows we've shipped.
Workflows we've shipped.
Different industries, same loop.
The cases below are anonymized capability patterns drawn from real engagements. Named references shared under NDA once we know what you're building.
RAG over docs + ticket triage
Problem
Tier-1 support drowning in repetitive tickets; docs scattered across Notion + Zendesk.
Approach
Claude agent with RAG over product docs and historical conversations. Confidence-routed: auto-reply if > 0.7, else escalate with a draft.
Claude Sonnet 4.6LangGraphPineconeZendesk
Outcome
42% Tier-1 deflection (B2B SaaS, 2026-Q1)
Abandoned-cart recovery agent
Problem
Generic recovery emails ignored; high-intent carts slipping through unaddressed.
Approach
Multi-step agent personalizes outreach using cart contents, customer history, and product margin. Hands off to human at $X threshold.
GPT-5-miniCrewAIShopifyKlaviyo
Outcome
+18% recovered revenue (ecommerce, 2026-Q1)
Invoice processing + exception triage
Problem
AP team manually extracting PO fields from vendor PDFs; exceptions taking days to resolve.
Approach
Vision-extract PO fields, validate against ERP, route exceptions to analyst with a draft resolution. 14-day pilot integrated to NetSuite.
Claude Sonnet 4.6pgvectorNetSuiteLangfuse
Outcome
76 hrs/wk AP time saved (logistics, 2026-Q1)
how this compares AI automation agency vs. the alternatives.
AI automation agency vs. the alternatives.
What you actually trade.
There are valid reasons to pick a no-code platform, hire an in-house team, run an ai automation consulting services engagement, or stick with intelligent process automation (RPA-style). There are also reasons to pick an ai automation company. Seven dimensions, honestly compared across an AI automation agency, an n8n agency, generic workflow automation consulting, and building in-house:
Dimension
You're here
GetWidget AI automation agency
Generic AI consulting Big-4 / boutique
No-code platforms Zapier AI · Make · n8n
Internal hires only Build the team yourself
Time to first live workflow From kickoff to production behind a flag.
GetWidget 6–8 weeks
Generic AI consulting 3–6 months
No-code platforms Days, for simple flows
Internal hires only 6–18 months ramp
Pricing signal What you actually see as a cost number.
GetWidget $ / workflow / month, reported
Generic AI consulting Slide deck, no per-unit cost
No-code platforms Per-task / per-zap pricing
Internal hires only Headcount only
Code ownership Where the workflow code actually lives.
GetWidget Your repo, your prompts
Generic AI consulting Vendor-owned, licensed back
No-code platforms Trapped in vendor canvas
Internal hires only Yours, if they don't leave
Model lock-in Can you swap Claude → GPT-5 → Llama?
GetWidget One variable swap
Generic AI consulting Vendor's pick, hard to change
No-code platforms Limited model menu
Internal hires only Team's call (if they exist)
Eval suite + observability How you know it works in production.
GetWidget Eval-first · Langfuse logs
Generic AI consulting Ad-hoc, varies by team
No-code platforms Run logs only, no evals
Internal hires only Depends on hire seniority
Cancellation / kill point When you can stop without losing the work.
GetWidget Walk-away at pilot · monthly
Generic AI consulting Annual retainer typical
No-code platforms Cancel anytime · keep canvas
Internal hires only Headcount cost is sunk
Best for Where this option actually shines.
GetWidget Production AI workflows w/ ownership
Generic AI consulting Strategy decks, exec alignment
No-code platforms Simple integration plumbing
Internal hires only Long-horizon platform building
Pricing and timelines reflect typical GetWidget engagements; competitor categories are generalizations from public pricing pages, sales conversations, and shipped client work.
Not sure which option fits?
A 30-minute fit call — we'll tell you honestly whether you need an agency, a platform, or a hire. No pitch.
frequently asked Questions we hear most.
Questions we hear most.
Real answers, no hedging.
What does an AI automation agency actually do?
An AI automation agency designs and ships AI-powered workflows for your business: sales follow-up agents, support deflection, document processing, internal copilots. We're hands-on engineers, not slide-deck consultants. We map your highest-ROI workflows in a two-week audit, build a pilot in six to eight weeks, and run the systems with you in production. Every workflow has a measurable outcome and a reported cost-of-ownership. The two-week audit is the same automation discovery and consulting engagement we run before any build commits. Vertical examples we ship most often: factory-floor automation (predictive maintenance + reorder agents) and education ops automation (grading queues + student-services workflows).
How does AI automation actually work?
Most production AI automation today is a combination of three things. (1) A foundation model (Claude, GPT-5, Gemini, or open-source like Llama) that handles natural-language reasoning. (2) Retrieval: pulling your private data (docs, tickets, CRM records) into the model's context via vector search, often with Pinecone, pgvector, or Weaviate. (3) Tools: letting the model call APIs in your stack to actually do things. We orchestrate these with frameworks like LangGraph and CrewAI, or write custom Python agents when the off-the-shelf options are over-engineered.
How long does it take to deploy an AI workflow?
From kickoff to first workflow live: typically six to eight weeks after a two-week audit. The first two weeks are discovery and pilot scoping, the next four to six are building and integrating, and the final week is production hardening: logging, eval suite, runbook, fallbacks. Some narrow workflows (e.g. document classification with a clean dataset) ship in two weeks; complex multi-system agents take eight to twelve. We don't quote two-week timelines for work that takes two months.
AI automation vs traditional automation: which is better?
They're complementary, not competitors. Traditional automation (Zapier, n8n, RPA) is best for deterministic, rule-based work: "when invoice arrives, save to drive." AI automation handles work that needs judgment: extracting data from unstructured documents, routing tickets based on intent, summarizing meeting transcripts, drafting replies in your tone. A well-built system uses both: the AI handles the messy reasoning, traditional automation handles the plumbing between systems.
What's the ROI of AI workflow automation?
We report cost-of-ownership monthly: dollars per workflow per month versus hours saved or revenue recovered. Typical patterns we've shipped: support deflection at $300–800 per month per workflow, saving 40+ hours of Tier-1 work. Invoice processing at $400 per month, saving 60+ hours. Abandoned-cart recovery at $200 per month with 15–20% revenue lift on the recovered cohort. ROI compounds because a shipped workflow keeps running while you build the next one.
Can you integrate AI with our existing software?
Yes. Most automation work is integration work. The AI part is often 10% of the project. We integrate against Salesforce, HubSpot, NetSuite, Slack, Microsoft Teams, Jira, Zendesk, Intercom, Shopify, Stripe, and most modern SaaS via their APIs. For older systems without APIs, we build adapters: SFTP, email, browser automation as a last resort. We never ask you to migrate platforms to do automation.
Which AI models do you use: OpenAI, Claude, or open-source?
All of them, picked per workflow. Claude wins on long-context reasoning and tool use; we use it for complex agents and document workflows. GPT-5 has the best ecosystem for structured outputs and function calling. Gemini is competitive on cost for high-volume classification. Open models (Llama, Mistral) run on your infra for cost-sensitive or compliance-sensitive workloads. The choice is a tradeoff per use case (latency, cost, quality, privacy) and we evaluate it openly.
What does an AI automation project cost?
Three engagement models. A workflow audit is fixed-fee for two weeks of discovery, ROI scoring, and a 90-day roadmap. A pilot-to-production engagement for a single workflow is fixed-bid, six to eight weeks. An ongoing automation team is monthly and includes a PM, AI engineer, and ops analyst shipping workflows on your roadmap. Cost-of-ownership for shipped workflows typically runs $200–$1,000 per workflow per month including model usage.
What are the best AI automation solutions for our business?
The right AI automation solutions depend on your highest-ROI workflows, not on what's trending. We see five categories pay back consistently. (1) Sales automation: lead qualification agents, outbound personalization, transcript-to-CRM, account research. (2) Operations and document workflows: invoice and PO processing, contract review, claim adjudication, exception triage. (3) Customer support automation: Tier-1 deflection with RAG, ticket routing, multilingual reply drafts. (4) Internal tools and copilots: meeting summarization, knowledge agents, Slack copilots, weekly digests. (5) Reporting and analytics: business digests, anomaly detection on KPIs, natural-language reporting. Most teams have a high-ROI candidate in three of these five categories. The audit ranks them so you don't have to guess.
How is AI for business automation different from traditional automation?
Traditional business automation (RPA, no-code, scheduled scripts) handles deterministic work where the rules are known up-front: "when invoice arrives, save to drive, update spreadsheet." AI for business automation handles work that needs judgment: extracting data from unstructured documents, routing tickets based on intent, summarizing meeting transcripts, drafting replies in your tone, qualifying leads against a written ICP. A production-grade system uses both: AI for the messy reasoning, traditional automation for the plumbing between systems. We're vendor-neutral on this; if a workflow is better served by a Zapier-style flow, we'll say so at the audit stage instead of building you a custom agent.
Ready to ship Stop building pilots.
Stop building pilots.
Start shipping AI that works.
Book a free 30-minute workflow audit. We'll identify two or three high-ROI automation candidates from your stack and give you a rough timeline + cost. No deck, no obligation to build.
30 min, async or live No NDA required You leave with a written roadmap
keep exploring Related pages.
Related pages.
Pick where you are.
Not sure if you need an automation agency, an integration partner, or just an agent build? These pages go deeper on each.
AI Agent Development
Multi-step autonomous agents using LangGraph and CrewAI.
AI Integration Services
Connecting AI to Salesforce, HubSpot, and your existing stack.
AI Consulting
Strategy and roadmap engagement before the build.
AI in E-commerce
Automation patterns for retail, marketplaces, D2C.
AI for Law Firms
Automation patterns for matter intake, contract review, e-discovery re-ranking.
AI Chatbot Development
Conversational front-ends layered on top of the automated workflow.
AI for Manufacturing
Manufacturing workflow automation: predictive maintenance, AOI re-screen, supply-chain reorder agents.
AI App Development Services
When automation grows into a full custom build — app shell, ML classifiers, agents, monitoring. The umbrella AI development services pillar.
Weekly eval gates
How we catch automation drift before it reaches production — frozen eval set, weekly replay, no-promote on regression.
Agent reliability benchmark (2026-Q3)
Dated benchmark on multi-step automation reliability — pass@1, mean steps, cost per task, error-recovery rate across Claude, GPT, Gemini, and open-source.
more on ai automation Four guides worth reading.
Four guides worth reading.
Customer service, sales-ops, platform picks, orchestration.
The long-form pieces our delivery team points new clients to when they're scoping an automation build.
Automated customer service architecture
Reference architecture, cost benchmarks, and the tier-1 deflection rubric we ship.
13-tool sales-ops workflow rubric
How we evaluated 13 sales-ops automation tools on real CRM workloads.
10-axis platform buyer rubric
The scoring framework we use internally before recommending build-vs-buy.
Claude agents in LangGraph
How we wire the agent orchestration layer underneath every automation.
Updated May 23, 2026 · By Navin Sharma