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AI AGENTS

Custom AI agents that ship to production.

We design, build, and deploy autonomous AI agents that integrate with your existing systems: CRMs, ERPs, banking cores, claims platforms, support stacks. Production-grade. Governance-ready. Audit-traceable.

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Certified·ISO/IEC 27001·ISO 9001·ISO/IEC 20000·ISO/IEC 42001·Startup India·MSME

4 active engagements·India and the UK·See them

How an agent is put together

A model, its tools, its memory, and the gates and audit log around all three.

How our agents are wired

Live runtime
Governed runtimeTamper-evident tracingContent moderationPrompt-injection defenseHuman-in-the-loop gating
  1. Trigger. Webhook, schedule, or human request.
  2. Plan. Goal decomposed into a tool sequence.
  3. Tool call. MCP tools with audit-traced arguments.
  4. Reflect. Result inspected: retry or escalate.
  5. Act. Committed, or handed to a human reviewer.
  6. Refine loop. Reflect can return to Plan when the tool call result requires a different sequence.
  7. Governed runtime. Every stage runs with tamper-evident tracing, content moderation, prompt-injection defense, and human-in-the-loop gating.

Stack

Models, orchestration, retrieval and evals, named.

Foundation models
Anthropic Claude (primary) · OpenAI GPT-4 family · Open-source (Llama, Mistral) for sovereign deployments
Orchestration
LangGraph · CrewAI · Custom orchestration where neither fits
Tool integration
MCP (Model Context Protocol) where supported · Custom function-calling otherwise
Memory & retrieval
Postgres + pgvector · Pinecone · Weaviate
Evaluation
Custom eval harnesses · LangSmith · Regression testing on production traffic
Compliance
Audit logging on every agent action · Human-in-the-loop gates · Prompt-injection defenses · Output content moderation

What we build

Four domains, and every agent in them integrates with systems you already run.

  • Banking & financial services agents

    Decisions with an audit trail: underwriting, fraud, KYC/AML and reconciliation agents that a regulator can follow.

    Underwriting assistants for NBFC loan processing · Real-time fraud detection and case-routing agents · KYC / AML document review and anomaly flagging · Reconciliation and settlement agents · Customer support agents with banking-grade compliance · Compliance monitoring and regulatory reporting agents

  • Healthcare agents

    Clinical documents read, coded and routed, with a clinician's sign-off before anything leaves the system.

    Clinical document extraction and ICD-10 / SNOMED coding · Prior-authorization automation · Insurance claim processing agents · Patient triage and appointment-scheduling agents · Lab report-generation and clinician sign-off agents (we built this for inferagen.ai)

  • E-commerce & retail agents

    Support, merchandising and inventory agents with real access to orders, returns and stock.

    Customer support agents with order / refund / returns access · Personalised merchandising and recommendation agents · Inventory reconciliation and demand-forecasting agents · Conversational commerce agents for WhatsApp / web / IVR

  • Operational agents (cross-industry)

    Copilots and document agents wired into the systems your teams already use.

    Internal copilots for engineering, support, sales, marketing · Document intelligence agents (contracts, claims, RFPs, compliance) · Data-analysis agents that query your warehouse and explain insights · Recruiter / sourcing agents · Browser-use agents for back-office automation

How we work with you

Start with a pilot on your data. Everything after it is optional.

  1. 014–6 weeks

    Agent pilot

    One specific use case, built on your data and run against an eval set agreed at the start.

    You leave with

    A working agent, an eval report and a go/no-go recommendation in writing.

    Book a discovery call
  2. 028–16 weeks

    Production agent build

    A validated pilot taken to production: integrations, tracing, gates, rollback and the governance pack.

    You leave with

    An agent in production with every action traceable to a person or a model version.

  3. 0312–24 weeks

    Multi-agent system

    Coordinated agents across functions, with one orchestration layer and one audit trail.

    You leave with

    A system your risk team can read end to end.

  4. 04Ongoing

    Agent operations

    Monitoring, evals, retraining and a governance review every month.

    You leave with

    An engineer on the agent, and a monthly record of how it behaved.

Five phases, with the artefact an agent needs at each one.

The full process
  1. 01

    Discovery & Strategy

    1-2 weeks

    Discovery brief with the agent's decision boundaries and the systems it may act on

    Signed off by you, before engineering starts

  2. 02

    Architecture & Design

    2-3 weeks

    Agent architecture: tools, memory, human-in-the-loop gates and the audit log, reviewed with your security team

    Signed off by your security team

  3. 03

    Agile Development

    4-12 weeks

    The agent, its eval harness and the code review trail

    Signed off by a senior engineer, every PR

  4. 04

    Quality Assurance

    2-4 weeks

    Eval report on production-like traffic, with red-team findings and the fixes

    Signed off by your QA and compliance teams

  5. 05

    Launch & Evolution

    Ongoing

    Tracing and drift dashboards, with a weekly digest

    Signed off by your team, every week

A build engagement runs 9-21 weeks to launch, then ongoing. Every phase ends with a named artefact and a named sign-off, under ISO/IEC 27001 and ISO 9001 controls.

Recent work on this line

Three engagements on this line now.

  • Lending / Banking

    Live in production

    AgenBTL: eight AI agents for UK commercial lending

    • United Kingdom
    • FCA · MCOB · UK GDPR
    • From the Lab

  • Life Sciences

    In active development

    Variant interpretation co-pilot for InferaGen.ai

    • ACMG classification
    • Clinician sign-off
    • From the Lab

  • ERP / Enterprise Software

    Active engagement

    AI-enabled automation inside an ERP transformation programme for a UK consultancy

    • United Kingdom
    • ERP
    • AI automation

How we work

Built like a product company, shipped like one.

  1. 01

    We build agents for ourselves, not only for clients.

    InferaGen.ai’s variant-interpretation co-pilot is an AI agent that drafts ACMG variant classifications for a clinician to approve, with human-in-the-loop gating and a full audit trail by design, rather than bolted on afterwards. It is in active development alongside the platform it sits in.

  2. 02

    Compliance heritage matters for AI agents.

    UK FCA, RBI, and US SEC are explicit: enterprises remain accountable for what their agents do. Most AI agencies build demos. We build for audit. Every agent we deploy has tracing, content moderation, prompt-injection defenses, and rollback paths.

  3. 03

    We don’t build chatbots and call them agents.

    A chatbot answers questions. An agent makes decisions, takes actions, integrates with systems, and is accountable for outcomes. Big difference. Most “AI agent” vendors blur the line. We don’t.

FREE FOR AI BUYERS

AI Agent Governance Checklist for Regulated Industries

A practical operator's checklist mapping UK FCA, RBI, and US SEC AI guidance to engineering controls. It is the same checklist we use on our own AI agent engagements.

  • Tracing, content moderation, and prompt-injection defenses
  • Human-in-the-loop gating and rollback paths
  • Audit logging on every agent action
  • Eval harnesses and regression testing on production traffic

32-page PDF · Email required

Request the checklist

Questions buyers ask

Straight answers, before the first call.

What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent makes decisions, takes actions in your systems, and is accountable for the outcome. That is why every agent we ship carries tracing, human-in-the-loop gates, content moderation, prompt-injection defences and a rollback path. A chatbot needs none of that; an agent that acts on a loan file or a claim needs all of it.
Which models and frameworks do you build on?
Anthropic Claude as the primary model, the OpenAI GPT-4 family where it fits, and open-source models (Llama, Mistral) for sovereign deployments that cannot call an external API. Orchestration in LangGraph or CrewAI, with custom orchestration where neither fits; tool integration through MCP where a system supports it and function-calling where it does not.
How do you keep an agent auditable?
Every agent action is logged with the inputs it saw, the tool it called and the model version that decided. Human-in-the-loop gates sit in front of any irreversible action. Evals run as regression tests on production traffic, and the whole engagement runs under ISO/IEC 42001, the AI management system standard we are certified against.
Can the agent run inside our own cloud account?
Yes. We deploy into your AWS, Azure or Google Cloud account, in an India region by default for data residency, and we can use open-source models hosted in that account when data cannot leave it. You own the infrastructure, the code and the logs from day one.
How long until a first agent is running on our data?
A pilot on one use case runs four to six weeks and ends with a working agent, an eval report and a written go/no-go. Taking a validated pilot to production is eight to sixteen weeks depending on the integrations.

Insights

From the blog.

Nothing filed under AI agents yet. These are the latest notes from the work.

All insights

Have an AI agent project in mind?

Book a 30-minute discovery call. We'll review your problem and sketch an agent architecture with the gates drawn in.

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+91 912-195-7728Hyderabad, IndiaEvery brief gets a senior review. Reply within 1 business hour, 9 AM-7 PM IST.