How do you keep our data safe in AI systems?

Data-boundary design comes first: what leaves your environment, what never can, and what must be redacted or tokenised in between. We deploy within your cloud where required, contract for zero-retention API tiers, and log every model interaction for audit.

What does an AI engagement cost and how long does it take?

A focused first phase — one workflow, production-grade — typically runs 8–16 weeks. Costs follow the blueprint from the Define phase. We deliberately start narrow: one workflow that works beats ten pilots that don't.

Which AI governance standards do you design against?

ISO/IEC 42001 for the management-system structure, the NIST AI Risk Management Framework for how risk is identified and tracked, and the EU AI Act's risk tiering where a client sells into Europe. We are certified to ISO 27001 and ISO 9001, not to 42001 — these are design references, not audits we have passed. In India the binding constraint is usually the DPDP Act 2023 plus whatever your regulator says about outsourcing, which is a different conversation and generally the one that decides the architecture.

How do you stop an AI system from making things up?

You cannot eliminate hallucination in a language model, so the engineering answer is to make it detectable and bounded rather than to promise it away. Retrieval with citations so an answer can be checked against a source. A scored evaluation set that runs on every change, so drift shows up as a number before a user notices. Confidence-based routing to a human lane below a threshold you set. And an event log that makes any answer reconstructable afterwards, which is what an auditor will actually ask for.

Practice

AI & Intelligent Systems

Strategy, custom LLM platforms, autonomous agents, and the MLOps backbone — AI engineered with the guardrails enterprises need and the outcomes boards expect.

Discuss this practice See related work

One workflow, production-grade: 8–16 wksOne workflow, production-grade
Model interactions logged for audit: 100%Model interactions logged for audit
Working software in your environment: Every 2 wksWorking software in your environment

The problem

Every enterprise now has AI ambitions; very few have AI in production doing consequential work. The gap isn't models — it's engineering: data plumbing, evaluation, guardrails, cost control, and integration into the systems where work actually happens. That engineering is our practice.

How we approach it

We treat AI like any other mission-critical system: discovery first, measurable success criteria, phased delivery, and a Prove phase your risk and compliance teams can interrogate. We build the version that still works in month eighteen.

Accountability

What this practice owns.

Not a scope of works — the things that stay our problem for as long as the engagement runs.

The data boundary — what leaves your environment, what never can, and what is redacted in between.

Evaluation: a scored test set that says whether a change made the system better or just different.

Guardrails and the audit trail, built as delivery phases rather than bolted on before go-live.

The unit economics — token spend per transaction, modelled before build and monitored after.

Capabilities

6 services in this practice

Each one is a full capability with its own page — the method, what you are handed, the stack, and who it is not for.

AI Strategy & Roadmap

A defensible plan for where AI creates value in your business — and where it doesn't.

A value-ranked AI portfolio instead of a pilot graveyard

Custom AI & LLM Development

RAG platforms, copilots, and document intelligence built on your data, inside your boundaries.

Answers grounded in your data, with citations your auditors can follow

AI Agents & Autonomous Workflows

Agents that execute multi-step business processes — supervised, audited, and integrated with your systems.

Queues that clear overnight instead of over weeks

ML & Predictive Intelligence

Forecasting, scoring, and anomaly detection embedded where decisions happen.

Forecast error cut enough to change purchasing behaviour

Computer Vision & Speech

Cameras and microphones turned into structured, decision-ready data streams.

Inspection coverage from sampled to total

MLOps & AI Infrastructure

The platform layer that keeps AI systems reliable, measurable, and affordable at scale.

AI releases gated by evidence, not vibes

What an engagement looks like

How this practice usually starts.

We do not publish prices — scope drives them, and a number without scope is a guess. Everything else about the shape, here.

Starts with: A paid discovery that ends in a scored feasibility call, not a pitch
Time to production: 8–16 weeks to the first workflow live
Who you get: An AI engineer, a platform engineer and an architect, with a data engineer where the pipeline needs one
Commercial model: Fixed-scope discovery, then phased build

What drives the number is on engagement models.

Typical stack

Opportunity scoring frameworks
Data audits
LLM feasibility spikes
Governance templates
Claude & GPT APIs
Open-source LLMs
Vector databases
Embedding pipelines
LangGraph
Evaluation frameworks
Agent orchestration (LangGraph)
Claude & GPT models
Workflow engines
Enterprise API integration
Observability stacks
Python ML stack
XGBoost & deep learning
Feature stores
Streaming inference
Explainability tooling
PyTorch vision models
Edge inference (Jetson/ONNX)
RTSP pipelines
Whisper-class ASR
Indian-language models
Kubernetes & GPU orchestration
vLLM & inference servers
Prompt registries
Observability (OpenTelemetry)
Cost analytics

Sectors

Where AI & intelligent systems comes up most.

BFSI & Fintech

Institutional-grade platforms for banks, NBFCs, insurers, and fintechs — proven at IRFC scale.

Healthcare & Life Sciences

Patient platforms, HIS integration, and care-workflow systems built consent-first.

Logistics & Supply Chain

Control towers, fleet visibility, and exception-first operations platforms.

Manufacturing & Industry 4.0

Custom ERP, shop-floor systems, and vision AI for plants that outgrew their spreadsheets.

Before you ask

Questions we hear about AI & intelligent systems

Whichever the problem and your constraints demand. Frontier APIs win on capability-per-effort; self-hosted open-source wins on data boundaries and unit economics at scale. Most enterprise architectures we ship blend both behind an abstraction layer, so you're never locked to one vendor's pricing.

Other practices

Next step

Bring us the problem. We will bring the architecture.

A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.

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No sales deck
An engineer on the call, not an account manager
NDA before you share anything