How We Build Agentic AI Inside Real Enterprise Stacks
Three engagements per sector across twelve industries – each one a description of the work, the systems involved, and how the day-to-day changes once it is live. Same engineering foundation across every engagement.
Built-in Assurances
BFSI — Agentic AI in Action
Document-heavy and audit-heavy by nature — claims, onboarding and fraud all benefit from the same pattern: confidence-calibrated extraction, contextual assembly, and human review reserved for the cases where it adds value.
3 engagementsTarget cluster: agentic AI in BFSI, AI insurance document intelligence, AI KYC onboarding workflow, AI fraud investigation copilot, OCR LLM claims processing.
AI Insurance Document Intelligence & Claims Processing
General insurance company, retail and commercial lines, EU, ~18-week engagement
Document intake, extraction, validation and claims triage as a confidence-calibrated pipeline — humans review what the system is unsure about, not every field.
Architecture & integrations
Policy documents, claim forms and supporting evidence arrive as scanned PDFs and phone-camera images of varying quality. Processors copy fields by hand into the policy admin system. Straightforward claims sit behind complex ones because everything goes through the same queue, and audit reconstructions are painful because the trail is scattered across email and spreadsheets.
Routine documents are processed without human transcription. Reviewers see only the fields the system is genuinely unsure about, which is where their judgment is actually useful. Clean claims move on a fast path instead of waiting in a single queue. And every decision has a trail behind it, which makes audits a normal conversation.
Ingestion Agent. Classifies incoming documents by type and routes them to the right extraction template; handles multi-page bundles and mixed-quality scans.
Extraction Agent. Pulls structured fields from OCR'd text with per-field confidence scoring; low-confidence fields surface to a reviewer instead of being written silently.
Validation Agent. Cross-checks extracted data against policy records, calculates expected values where possible, and flags inconsistencies before they reach the policy admin system.
Triage Agent. Routes clean claims through a fast path and exceptions to specialists with the relevant context already assembled.
AI KYC & Customer Onboarding Workflow
Digital bank serving retail customers, EU, ~14-week engagement
Customer onboarding as an agentic workflow — identity verification, sanctions screening, risk scoring and account opening, with human review reserved for genuine edge cases.
Architecture & integrations
Onboarding a new customer requires document collection across channels, manual identity checks, sanctions and PEP screening on rotating lists, and a risk decision that pulls context from several systems. Throughput is capacity-bound. Customers drop off during multi-day waits, and the ones who do not drop off arrive at the front line with patience already spent.
Clean customers onboard in one sitting. The compliance team works on the cases that need judgment instead of every case. Every decision has a documented trail behind it — what was checked, what was found, what was decided — which means audits read a ledger instead of reconstructing one.
Document Agent. Receives identity documents, proof of address and supporting documentation across the onboarding channels; validates document authenticity through eIDV providers and extracts structured fields with per-field confidence.
Verification Agent. Runs identity verification, sanctions and PEP screening, and adverse media checks through the bank's provider stack; surfaces hits with rationale and recommended next steps.
Risk Agent. Assembles the risk picture — verification results, behavioral signals, declared profile — and recommends the appropriate onboarding decision (clean, enhanced due diligence, manual review, decline) with traceable reasoning.
Coordination Agent. Coordinates the customer-facing conversation across the onboarding journey — asks for missing documents in plain language, communicates the decision, handles next steps.
AI Fraud Investigation Copilot
Card issuer with retail and SME card portfolios, EU, ~16-week engagement
A fraud-investigation copilot for analysts — alerts arrive with the case already assembled, linked groupings already surfaced, and a recommended disposition with cited rationale, so the analyst decides rather than gathers.
Architecture & integrations
Fraud alerts arrive at the analyst's screen as a list of transactions and a score. Investigation means pulling cardholder context, merchant context, transaction history, prior cases, network signals — across a dozen tools — to decide. Backlogs grow; high-value cases sit behind volume; the cases that are genuinely fraudulent and the ones that are clean false positives look identical until investigated.
Analysts decide instead of gather. Cases arrive with the picture already assembled — and the groupings that ring across multiple alerts are visible immediately, not after the third case. Decisions are documented as a ledger, not as case notes scattered across tools.
Triage Agent. For every alert, assembles the full context — cardholder profile, transaction history, merchant context, prior cases, network signals — into a single case packet ready for analyst review.
Linking Agent. Connects the alert to other recent alerts that share signals (same cardholder, merchant, IP range, device fingerprint), and surfaces those groupings so the analyst sees the wider picture, not just one transaction.
Recommendation Agent. Suggests a disposition — clear, monitor, block, escalate — with rationale traced to the underlying signals; the analyst confirms or overrides.
Action Agent. Executes confirmed actions — card blocks, customer notifications, case filings — with full audit and per-action confirmation; no autonomous action against customer funds.
Legal — Agentic AI in Action
Contracts, research and discovery are all reading-and-reasoning work over large document sets — exactly the work where LLMs with retrieval and human-in-loop review earn their place. We design the layer; the lawyer remains the decision-maker.
2 engagementsTarget cluster: agentic AI in legal, AI contract review automation, AI legal research assistant, AI eDiscovery triage, LLM for law firms.
AI Contract Review & Playbook Compliance Assistant
Mid-sized commercial law firm serving SME clients, EU, ~11-week engagement
Inbound contracts read against the firm's playbook in minutes — deviations flagged, suggested redlines drafted, the partner deciding rather than reading from scratch.
Architecture & integrations
Junior associates spend hours per contract reading inbound NDAs, MSAs and SOWs against the firm's standard playbook. Deviations get spotted unevenly depending on who reviewed. Partners end up either re-reading what associates already reviewed or trusting an inconsistent first pass. Turnaround on simple agreements stretches longer than the client expects.
Associates open a marked-up draft with deviations already mapped to the playbook, not a blank document and a contract. Partners spend their time on the genuinely material clauses and the client conversation. Simple agreements move on a fast path; complex ones get the partner's attention earlier in the cycle.
Reading Agent. Reads the inbound contract, identifies the document type and the controlling jurisdiction, and structures the clauses against the firm's clause taxonomy.
Playbook Agent. Compares each clause against the firm's playbook positions — fall-back, market-standard, walk-away — and tags every deviation with the relevant playbook reference.
Redline Agent. Drafts suggested redlines for deviations in the firm's preferred style; the associate or partner accepts, adjusts, or escalates per matter.
Risk Summary Agent. Produces a one-page risk summary for the partner covering material deviations, unusual terms and recommended client questions.
AI Legal Research & Case Synthesis Assistant
Litigation practice within a regional law firm, India, ~10-week engagement
Case-law research and synthesis as an agentic workflow — search runs across the firm's research subscriptions, findings are synthesized with citations, and a research memo is drafted for the associate to refine.
Architecture & integrations
Associates spend the first day or two of any new matter doing background research — searching across case-law databases, reading judgments, pulling threads forward, and assembling a working note. Findings rarely make it back into a structured form the next matter can use. The same case gets re-found by a different associate three months later.
Associates open a research memo with cited authorities and a working synthesis, not a blank document and a search box. The firm's collective research stops evaporating into individual matter files. Junior associates climb the learning curve faster because they read good drafts before producing their own.
Search Agent. Translates the matter's legal question into structured queries across the firm's research subscriptions and open sources; collects relevant judgments and authorities.
Synthesis Agent. Reads collected authorities, identifies the controlling principles, distinguishing facts and reasoning, and assembles a synthesis with every assertion cited to the source paragraph.
Drafting Agent. Drafts a research memo in the firm's house style — issue, applicable law, analysis, conclusion — for the associate to refine.
Knowledge Agent. Files the completed research into the firm's matter knowledge base so the next associate working a related question starts from a stronger base.
Frequently Asked Questions
Clear info on cost, IP, staffing, time zones, and compliance.
Frequently Asked Questions
Clear info on cost, IP, staffing, time zones, and compliance.
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