AI engineering by industry

Production AI shaped around your operating environment

Each industry sets different limits on data, autonomy, human approval, system access, and audit evidence. We design agentic AI systems around those limits, then strengthen the engineering pipeline that builds and operates them.

Explore industries

The operating boundary changes

Select an industry to see the limits the system must respect.

Data boundary
Customer, catalogue, inventory, order, payment, and support data
Human authority
Bounded pricing, promotion, refund, and customer-service actions
Audit evidence
Recommendations, actions, approvals, reversals, and revenue outcomes
System surface
Commerce platforms, ERPs, CRMs, WMS, payments, and support tools

Choose your operating environment

Start with the industry closest to your systems, constraints, and buyers.

E-commerce & Retail

Connect AI to merchandising, customer service, inventory, and commerce systems while keeping actions measurable and reversible.

  • Customer operations
  • System integration
  • Peak reliability
  • Action reversal
Explore industry

Financial Services & FinTech

Build AI systems that act across regulated workflows while preserving authority boundaries, audit evidence, and sensitive-data controls.

  • Runtime governance
  • Auditability
  • Data residency
  • Human oversight
Explore industry

Healthcare & Life Sciences

Introduce AI into clinical, administrative, and research workflows while preserving patient privacy, human oversight, and validation.

  • Patient data
  • Human review
  • System validation
  • Integration evidence
Explore industry

HR & Workforce Technology

Apply AI across recruitment, employee support, workforce operations, and HR products with clear privacy and decision controls.

  • Sensitive data
  • Decision oversight
  • Workflow automation
  • Audit evidence
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Professional Services

Turn institutional knowledge, research, documents, and repeatable delivery methods into governed AI workflows.

  • Knowledge retrieval
  • Document workflows
  • Quality control
  • Source provenance
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Software & Technology

Improve software delivery after AI accelerates code generation but review, testing, documentation, and governance remain constrained.

  • Sprint throughput
  • Code quality
  • AI governance
  • Developer workflow
Explore industry

Industry constraints shape the architecture

We choose the architecture around the decision, its owner, the required evidence, and the systems involved.

The decision

Name the actions the system may recommend, approve, change, or execute. A customer answer, account action, candidate recommendation, clinical summary, and software release carry different consequences.

The authority boundary

Separate actions the runtime may allow from those that require approval or must remain human decisions. Policy and code hold that boundary outside the model's reasoning.

The evidence requirement

Record the sources, model and prompt version, policy decision, tool activity, reviewer action, and final observed outcome needed to reconstruct each consequential run.

The operating environment

Choose the deployment, integration, identity, and data architecture around the systems already in use. Regulation and internal policy limit where information may travel and which interfaces the agent may reach.

One production discipline across industries

We assess the idea, build the system, enforce its controls, and run it against a published operating model.

  1. 1

    Assess

    Test the workflow, data, integration surface, risk, and expected return before committing to a build.

    Review the feasibility study
  2. 2

    Build

    Design the architecture, connect the systems, and implement a bounded workflow against real material.

    Explore agentic AI systems
  3. 3

    Control

    Define permissions, pre-action gates, human-in-the-loop approval, containment, and the evidence retained for each decision.

    See governance and risk
  4. 4

    Operate

    Run behavioral evals, observe complete runs, control cost, and manage agentic drift after deployment.

    See the operating model

Financial services production system

About 500,000 banking conversations per month under a governed runtime

A fintech platform serving banks needed more support capacity. Runtime governance kept authority over sensitive customer workflows, while the agent handled documented requests. The client identity is withheld under NDA.

Explore the financial-services system

Client-reported results after 12 weeks

Autonomous resolution 78%
First response 2 to 4 hours under 5 min
Cost per ticket ₹500 ₹150
Customer satisfaction 3.2 4.4 / 5

PCI-DSS environment · India data residency · Full decision audit trail

Engineering pipeline bottlenecks need a different toolset

AI coding tools increase the volume of code entering review. PASSR, TESTR, DOCKR, and VCT address the review, testing, documentation, and governance work that follows.

PASSRReview AI-generated changes before the queue absorbs senior engineering time. TESTRGenerate and maintain tests as code volume and change velocity increase. DOCKRKeep technical documentation aligned with the software and its architecture. VCTTurn faster individual coding into measurable sprint-level improvement.

Technical context for industry AI decisions

These guides explain the controls behind the industry work: governance, evaluation, observability, and operation.

Identify the first workflow worth putting into production

We will examine the decision, data, integrations, authority boundary, expected return, and failure modes before recommending a build.

Reviewed by Jayaveer Bhupalam, Founder & CTO Last updated September 24, 2026