Enterprise copilots
Assistants grounded in your policies, contracts, wikis and tickets, with citations and access control that follows your existing permissions.
GenAI & AI Agents
Generative AI and AI agents that work in production.
We build copilots, retrieval systems and agents that read your documents, use your tools and hand off to people when they should. Every system ships with evaluations, guardrails and monitoring, so it behaves like any other critical piece of software.
The problem
Why this work usually stalls.
01
Demos that never ship
A chatbot that impresses in a meeting often fails on real documents, real permissions and real edge cases. Most GenAI work stops there.
02
No way to measure quality
Without an evaluation set, nobody can say whether the system got better or worse after a change, so every release is a guess.
03
Answers without sources
People will not trust an assistant that cannot show where an answer came from, and auditors will not either.
04
Agents with too much freedom
An agent that can act on systems needs clear limits, approvals for risky steps and a full log of what it did.
What we deliver
GenAI & AI Agents, built for production.
Pick one piece or the whole programme. Each is scoped to a business metric and shipped, not left as a proof of concept.
Document AI
Pipelines that read invoices, statements, claims and forms, extract what matters and route only the true exceptions to a person.
AI agents and workflows
Agents that read, classify and act on emails, tickets and records through your APIs, with humans approving the risky steps.
Evaluation and guardrails
Test sets built from your real cases, automated scoring on every change, and checks for leakage, toxicity and off-policy actions.
Model choice and fine-tuning
We compare commercial and open-weight models on your data and fine-tune only when retrieval and prompting are not enough.
GenAI features in your product
AI features inside your own SaaS or app, designed for latency, cost per request and graceful failure.
How we deliver
From first call to production.
Every phase ends with something you can use, not just something you can read.
Always included
- A senior squad of two to four people, led by an engineer from companies like Amazon, Google or Microsoft
- Weekly demos and one business metric agreed upfront
- Founder review every week and sign-off before production
- All code, models and documentation owned by you
Typical tools
01
Weeks 1 to 2
Use case and data review
We pick the workflow where GenAI saves the most time and check the data it depends on.
- Map the current workflow and who owns it
- Collect real examples to build the first evaluation set
- Agree the one metric that defines success
02
Weeks 2 to 3
Design and evaluation plan
Architecture, model options and the test bar are agreed before production code.
- Retrieval, permissions and data flow designed
- Models compared on your evaluation set
- Guardrails and human approval points defined
03
Weeks 3 to 8
Build and pilot
A senior squad ships weekly with your team, against the evaluation set.
- Working pilot with real users and real data
- Scores tracked on every change
- Logging, tracing and cost tracking in place
04
Ongoing
Scale and run
We harden the system, roll it out and keep it improving.
- Monitoring for quality, drift and cost
- Feedback loops from users into the evaluation set
- Hand-off with runbooks, or a run-and-improve retainer
Where we apply it
Industries we know well.
Further reading
From the blog.
RAG vs Fine Tuning: How to Choose for Enterprise GenAI
RAG gives a model the right knowledge; fine-tuning changes how it behaves. A clear comparison and decision guide for choosing between them, or using both.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
AI Implementation Cost in India: Drivers, Ranges and Budgeting
A practical guide to AI implementation cost: the real cost drivers, indicative ranges by project type, the running costs people forget and how to budget sensibly.
FAQ
Questions we hear a lot.
Should we use RAG or fine-tune a model?
Most business use cases start with retrieval (RAG), because it keeps answers grounded in your current documents and is cheaper to change. Fine-tuning helps when you need a specific style, format or behaviour that prompting cannot reach. We test both on your data before recommending either.
Can you use open-weight models inside our own cloud?
Yes. We are platform agnostic and regularly compare commercial APIs with open-weight models such as Llama or Mistral. When data must stay in your environment, we deploy models inside your own cloud account.
How do you stop the system from making things up?
We ground answers in your sources and show citations, refuse to answer when the sources do not support a claim, and measure this on an evaluation set built from your real questions. Risky actions by agents always need human approval.
How long does a GenAI pilot take?
Typically a two-week discovery followed by a production pilot in four to six weeks, with real users and real data. The exact timeline depends on data access and integrations.
Other services
Everything between ambition and production.
AI Strategy & Roadmaps
Find where AI moves your numbers and sequence a roadmap people believe in.
Data Platforms & Engineering
Lakehouses, clean pipelines and governed metrics that every model depends on.
Analytics & Decision Intelligence
Forecasting, pricing, churn and demand models, plus dashboards people open.
MLOps & AI Engineering
CI/CD for models, observability and cost controls that keep AI reliable.
Digital Transformation & AI Governance
Process redesign, change and responsible AI aligned with the DPDP Act and EU AI Act.
Managed Services
Cloud, DevOps, QA and release management, run 24x7 against clear SLAs.
Talk to a founder about GenAI.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.