Eval-first scoping
Every pilot opens with a frozen golden set of 100–500 labelled examples. The eval gates the cutover; no eval, no production. Regression-tested in CI on every prompt or model bump.
industries AI by industry, built for your
AI by industry, built for your
stack and compliance regime.
Ten verticals shipped, each on its own integration spine and regulatory posture. Healthcare on Epic FHIR with a BAA. Fintech on FFIEC-supervised PrivateLink. Legal with privilege tagging before the first document enters retrieval. E-commerce on Shopify and the OMS. Education on LTI 1.3 + FERPA scope. Manufacturing on SAP and OPC UA with no autonomous PLC writeback. Travel on Amadeus and NDC. Real estate on Yardi and MLS. Insurance on Guidewire. HR on Workday with EEOC bias audits. Pick yours. The compliance posture and integration know-how are already in the team.
Definition
What industries does GetWidget ship AI into?
GetWidget ships AI for 10 industry verticals, each with its own compliance posture, integration stack, and proven workflow patterns. Healthcare runs under HIPAA on AWS Bedrock plus PrivateLink; legal runs under privilege-aware controls with iManage and NetDocuments; fintech runs under FFIEC and SR 11-7 with PrivateLink deployment; e-commerce runs against Shopify and Algolia; education runs under FERPA inside Canvas, Blackboard, or PowerSchool; manufacturing runs read-only against OPC UA OT stacks; insurance runs inside Guidewire and Duck Creek under NAIC Model Bulletin disclosure; HR runs under EEOC and NYC AEDT Law 144 inside Workday; real estate runs against MLS feeds and Yardi/AppFolio under Fair Housing Act controls; travel runs against Amadeus and Sabre GDS APIs. Every industry deployment follows the same engagement model: a fixed-fee discovery audit, a fixed-bid 4-6 week pilot with vertical-specific eval criteria locked in week 1, and monthly continuous engagement after.
the industry framing problem
"AI for healthcare" isn't a thing. But AI for your healthcare system is.
Most AI-development vendors pitch verticals the way a hotel chain pitches cities: "we do healthcare, legal, fintech, ecommerce." It's a website tab, not a posture. Drill in and the playbook is the same generic chatbot, the same generic RAG, the same generic agent loop, with a different stock photo on the case-study card.
What actually matters to a buyer in a regulated or operationally complex vertical is far more specific. Which EHR does the engagement integrate with: Epic, Cerner, Athena, or a custom HL7 v2 spine? Which core-banking clearing rail does the fraud agent touch: card, wire, ACH, RTP? Which case-management system holds the privileged documents: iManage, NetDocuments, Relativity? Which MLS does the real-estate copilot pull from, and which Fair Housing posture covers the tenant-screening output? Which LMS does the academic advisor speak LTI to: Canvas, Blackboard, Moodle, Google Classroom?
And underneath the integration: which compliance regime is the primary one? HIPAA changes the data-flow shape. FFIEC changes the deployment posture. Privilege changes the retrieval prompt. FERPA changes the corpus scope. The Fair Housing Act changes the model output. NAIC AI Model Bulletin changes the explainability surface. None of these are checkboxes. They reshape the system end-to-end.
Vertical-specific playbooks beat generic posture every time. The 10 pillars below are the ones where we have shipped, named references available under NDA, and the integration + compliance know-how already lives in the team. Pick yours and skip the discovery tutorial.
ten live industry pillars Ten industries we've shipped in.
Ten industries we've shipped in.
Pick yours.
Each pillar is a full-depth services page: six AI workflows, model selection rationale, engagement tiers, signature architecture diagram, and a compliance callout specific to the vertical. Open any card for the full pillar.
Healthcare
Ambient scribes, prior-auth automation, AI medical billing, and triage chatbots on Epic, Cerner, and Athena. PHI-scrubbed, audit-logged, BAA before any data touches the stack.
Fintech
Fraud agents, KYC tiering, credit decisioning, and treasury copilots inside FFIEC-supervised banks. PrivateLink-deployable, policy-as-code gated, 2-eye review on every disposition.
Legal
Contract review, e-discovery TAR + GenAI re-ranking, matter intake, and AI paralegal workflows. Privilege-tagged before any document enters a prompt; ABA Op. 512 citation chains.
E-commerce
Agentic commerce, inventory copilots, dynamic pricing, and voice copilots in mobile apps. Conversion uplift with fulfillment-aware context; cost-per-call math published monthly.
Education
AI tutoring, essay grading, quiz generation, and LMS-integrated academic advisors on LTI 1.3. FERPA + COPPA + IDEA scoped, teacher-in-loop flag on every workflow.
Manufacturing
Predictive maintenance, visual inspection, supply-chain agents, and production scheduling on SAP and OPC UA. Human-in-loop gate enforced. No autonomous PLC writeback, ever.
Travel
AI booking, trip planning, hotel concierges, and revenue management for OTAs, hotels, airlines, and TMCs. Ships on your GDS or NDC stack with revenue-manager sign-off on pricing agents.
Real Estate
Brokerage lead-to-tour nurture, multifamily leasing AI on Yardi and AppFolio, CRE underwriting copilots, AI tenant-screening triage. Fair-Housing-Act-aware, FCRA-conscious.
Insurance
Claims triage agents, FNOL voice copilots, underwriting assistants, and policy-doc RAG on Guidewire and Duck Creek. NAIC AI Model Bulletin-aligned; adverse-action explainability baked in.
HR
Recruiting screeners with EEOC bias audits, onboarding copilots, internal-mobility advisors, and people-analytics RAG. NYC Local Law 144 + EU AI Act high-risk posture, audit-logged.
Not sure which vertical fits?
Book a free 30-minute audit. We map your compliance exposure and system-of-record before recommending a pillar.
Book a free audit → side by side Industry playbooks,
Industry playbooks,
lined up.
At-a-glance view of how each playbook differs across regulator, system of record, pilot length, workflow shape, and audit posture. If your industry isn't here, scan the closest neighbour: the patterns transfer.
| Attribute | Healthcare | Legal | Fintech | E-commerce | Manufacturing |
|---|---|---|---|---|---|
| Compliance regime The regulator you answer to first. | HIPAA + state DOH | Privilege + FRE 502 | FFIEC + state DFS | PCI DSS + state CPRA | ISO 9001 + OSHA |
| Primary integration The system of record we wrap. | Epic FHIR R4 | iManage / NetDocs | Core banking + KYC | Shopify / OMS | SAP + OPC UA |
| Typical pilot Sprint to shadow-mode. | 8–10 weeks | 6–8 weeks | 9–12 weeks | 4–6 weeks | 8–12 weeks |
| Workflow shape Where AI lands in the org. | Pre-clinician draft | First-pass review | Analyst triage | Customer-facing | Operator copilot |
| Audit posture Who sees the logs. | Compliance + DOH | Partners + counsel | Internal audit + reg | Ops + finance | Plant manager + EHS |
* Pilot lengths are typical for the first-shipped workflow per vertical; complex engagements run longer.
Three case studies, three regulators, one playbook.
They came in already knowing the BAA dance, the audit-log retention rules, and the read-only-first posture our reviewers wanted. We didn't have to teach them what FHIR R4 was. The pilot landed in shadow mode in nine weeks.
HIPAA-safe clinical triage agent, shipped in 9 weeks
First-pass MSA review with privilege-aware clause RAG
Claude fraud agent on AWS PrivateLink
constant across every vertical What every vertical gets,
What every vertical gets,
regardless.
Whether we ship in HIPAA, FFIEC, FERPA, or Fair Housing, four things never change: the eval-first scoping, the audit logging, the named walk-away kill points, and the model-agnostic orchestration layer. These are the defaults, not the add-ons.
Audit logging
Every call, every retrieval span, every tool invocation logged for replay and dispute. Retention tuned to the regulator (7 yr for HIPAA, 7 yr for SEC, etc.). PII-redacted at write.
Walk-away kill points
Before week 1 of any pilot, we name the single metric we'd kill it for. Triage wait time. Groundedness. Precision @ 1% FPR. If it doesn't move, we don't ship — and you didn't waste budget.
Model-agnostic stack
Claude, GPT, Gemini, Llama, picked per workflow on cost-vs-quality math, not preference. The orchestration layer abstracts the call; you can re-route a workflow with a config change.
regulated onboarding
How we onboard a regulated client: paper before prompt.
The first two weeks of a regulated engagement are paperwork, not code. Mutual NDA the day of the audit call. Data Processing Agreement and HIPAA Business Associate Agreement (or the equivalent vertical-specific paper: DPA + NAIC attestation for insurance, MSA + privilege-handling addendum for legal) signed before any production data leaves the client's environment. Cyber and E&O certificates of insurance exchanged. Vendor security questionnaire returned, including our SOC 2 attestation and the sub-processor list.
Then we go read-only first. The pilot ingests production data (synthetic copies where the regulator requires de-identification, real data where the BAA covers it) but writes nothing back. Every output is logged to an audit table the client's compliance team can query directly. Every retrieval span is logged with the privilege state, the patient or matter identifier, and the timestamp. Nothing touches a downstream system of record yet.
From there we bake in shadow mode for 4–8 weeks. The model produces what it would have produced, the human reviewer produces what they would have produced anyway, and the two are diff'd into the eval table. The frozen golden set ratchets up; regressions get caught in CI. Only after the eval clears the cutover bar, and the client's compliance team has signed off, do we open the gated writeback path, with 2-eye guardrails on every high-stakes call.
None of this is novel. It's what regulated buyers have done for two decades with any new vendor system. The mistake AI vendors make is assuming "the model is intelligent" lets them skip the choreography. It doesn't. The choreography is the product.
The engagement shape stays consistent across all 10 verticals: a fixed-fee discovery audit upfront, a fixed-bid pilot that ships in 5 to 8 weeks against the vertical's compliance bar, then continuous delivery on a monthly cadence with the embedded AI team owning eval gates, drift detection, and cost-of-ownership reporting.
frequently asked What regulated-vertical buyers ask first.
What regulated-vertical buyers ask first.
Direct answers, no hedging.
Do you sign BAAs / HIPAA agreements?
Yes. We sign a HIPAA Business Associate Agreement before any PHI touches our stack (covered-entity or business-associate side, your paper or ours). We also carry the cyber + E&O coverage covered entities typically require, and we run the engagement on a customer-managed VPC with PrivateLink so the data never crosses a shared SaaS boundary. Sample BAA available under NDA on the audit call.
Have you shipped inside a clearing-house or FFIEC-supervised bank?
Yes. The fraud-agent case study is a US mid-market bank with full FFIEC IT-exam exposure. The deployment posture is AWS PrivateLink, the model is called over a customer-managed endpoint, the policy-as-code gate is reviewed by their internal audit and second-line risk before any production traffic. We've done KYC tiering and treasury copilots under the same posture; we can share architecture diagrams under NDA on the audit call.
Are you privilege-aware for legal work?
Yes. Every document in a legal RAG corpus is tagged with a privilege state (privileged · work-product · ordinary · public) before it enters retrieval, and the prompt is constructed so the model never sees a span outside the requesting matter's privilege scope. Outputs are logged with the privilege state attached for FRE 502(d) clawback defensibility. ABA Op. 512 citation chains are forced via regex on the answer schema.
Can we deploy on AWS PrivateLink / Azure private endpoint?
Yes. Both are first-class deployment postures. Anthropic, OpenAI, and the major hosted-model vendors all offer PrivateLink or private-endpoint access; we wire the orchestration layer (LangGraph, retrieval, policy gates) inside your VPC so the only thing leaving is the model call itself, over a private route. The fraud-agent and clinical-triage cases both run this posture in production.
What does 'industry-specific' actually mean? Do you re-train models?
Almost never. 'Industry-specific' here means industry-specific retrieval corpus, industry-specific prompt schema, industry-specific compliance posture, and industry-specific eval set, not a fine-tuned foundation model. Frontier base models are good enough that fine-tuning is rarely the ROI move; what wins is grounding them in your corpus and gating them with your policy. We'll fine-tune (LoRA or full) when the math supports it, which is roughly 1 in 8 engagements.
We're in a niche industry not listed here. Can you ship?
Probably yes. The 10 pillars on this page are where we have shipped, documented case studies, and named-reference clients. The patterns transfer: eval-first scoping, system-of-record integration, compliance-posture-first design, walk-away kill points. We've also shipped quietly in adjacent verticals (logistics, energy, public sector) without dedicated pillar pages. Book the audit; if the workflow is case-study-shaped, we'll scope it. If it isn't, we'll tell you and recommend an off-the-shelf platform.
Ready to pick a vertical Start with a free
Start with a free
vertical-specific audit.
Tell us your industry and your highest-friction workflow. We review your compliance exposure, map the integration surface (EHR · ERP · GDS · LMS · MLS · core banking, whichever applies), recommend a model + retrieval recipe, project token + run cost, and scope a 4–12 week pilot. No deck, no obligation to build.
30 min, async or live Compliance surface mapped in the call Walk-away point baked into the pilot
keep exploring From the industries hub
From the industries hub
back into the pillars.
Every industry pillar feeds back into a service or proof page. Start anywhere.
Services hub
Every service pillar: AI development, agents, voice, chatbots, RAG, governance, Flutter.
Case studies
Six published engagements across healthcare, legal, fintech, SaaS, and e-commerce with eval data.
AI Governance
Policy-as-code, audit logging, NIST AI RMF mapping, and EU AI Act readiness for regulated verticals.
Claude Development
Claude Sonnet 4.6 + Haiku 4.5 specialists. The team behind the fraud, triage, and contract-review cases.
About
Who we are, how we run engagements, and why we publish the eval math instead of slideware.
AI Automation Agency
The horizontal automation pillar that shows up across every vertical on this hub. Same loop, different domain inputs.