A prototype nobody can ship
It answers beautifully in a notebook, then meets real traffic, real permissions and real edge cases. There is no route from that demo to something on-call can support.
The logic behind the agent
Production‑grade AI, engineered to ship.
We design and build LLM and multi-agent systems — and the cloud architecture that keeps them fast, observable, and cost-controlled once real users arrive.
- Years building
- 7+
- Projects delivered
- 30+
- Delivery regions
- US & IN
Built with
- LangGraph
- FastAPI
- Kubernetes
- Azure
- AWS
- Terraform
- Pinecone
- Postgres
Use cases
What we could build for your team
Pick a team and see the shape of the system. Same building blocks every time — a trigger, an agent with its model and tools, a decision, and somewhere the answer lands.
The hard part is not the agent — it is the guardrails around it. Permissions are checked before anything is planned, destructive actions cannot be approved by one person, every run emits traces and a diff, and the validator re-plans itself when a dry run fails.
Nothing reaches a customer unreviewed. Low-confidence tickets never get a draft at all — they route to a person, which is what keeps trust in the system.
You set the confidence bar, so you choose how much lands automatically. Every extracted field keeps a pointer back to the page it came from.
The agent decides which tools to call and runs them in parallel. When sources disagree it surfaces the conflict rather than averaging it away.
Permissions are enforced at retrieval, so people only ever see what they already have access to. Stale pages get down-ranked instead of quietly misleading everyone.
Before
The demo worked. Then it met production.
Most AI work we get called into has already been built once. It stalls in the same three places every time.
No way to tell if it improved
Without an evaluation set, “better” is a matter of opinion. Every prompt change is a gamble, every regression is found by a customer, and nobody can say which version to trust.
Costs that scale the wrong way
Token spend and latency grow faster than usage does. The pilot was cheap enough to wave through; the rollout quietly becomes the largest line in the budget.
After
Built to survive contact with real users
We treat an AI feature like any other production system: scoped before it is built, measured while it is built, and handed over so your team owns it afterwards.
- Fixed scope before codeAn architecture and a proposal, not an open-ended retainer
- Evaluations from day oneQuality is a number you can watch, not a feeling
- Handover, not lock-inDocs, dashboards and a walkthrough with your engineers
Capabilities
Three practices, one delivery team
Most engagements start in one and grow into the next — from a proof of concept, to a system in production, to the data work that keeps improving it.
AI Agents & GenAI Systems
LLM applications that hold up outside the demo — grounded in your data, measured, and guarded against the failure modes that surface in production.
- Multi-agent workflows
- RAG pipelines
- Vector search
- Evals & guardrails
- Tool calling
- Token & cost control
- LLM observability
- Knowledge search
Cloud-Native Product Engineering
The unglamorous half that decides whether an AI feature survives launch: services, pipelines, deployment, and infrastructure you can hand to a team.
- FastAPI · Node · Django
- Microservices & APIs
- Docker · Kubernetes
- CI/CD
- AWS · Azure · GCP
- Terraform
- Web & app builds
Data Intelligence & Research
Evidence before opinion. Modelling, experimentation, and benchmarking that tell you which approach is actually worth building.
- EDA & analytics
- Predictive modelling
- NLP pipelines
- Data engineering
- A/B testing
- Model benchmarking
How we work
A short path from idea to production
No open-ended discovery. Each phase ends in something you can review, run, or hand to your team.
-
01
Scope
A working session on the problem, data, and constraints. You leave with an architecture and a fixed-scope proposal.
-
02
Prototype
A narrow end-to-end slice on your real data, with an evaluation set — so the decision to continue is based on numbers.
-
03
Build
Production hardening: infrastructure, CI/CD, guardrails, cost controls, and the tests that keep quality from drifting.
-
04
Hand over
Documentation, dashboards, and a walkthrough with your engineers. Ongoing support is optional, never a dependency.
Selected work
Systems running in production
Client names withheld under NDA. Happy to walk through the architecture and trade-offs on a call.
Enterprise · Support operations
Knowledge RAG over 200K+ documents
A retrieval layer across scattered internal documentation, surfaced where support agents already work rather than in yet another tool.
40%
reduction in ticket resolution time
- Hybrid retrieval with reranking
- Slack bot + embedded web UI
- AWS, autoscaled
Product · Research automation
Multi-agent research assistant
A LangGraph orchestration where planning, web search, and summarisation agents hand work between each other under an explicit state machine.
Typed state machine
every hand-off between agents is explicit and replayable
- Search & summarisation agents
- Structured tool calling
- Azure Kubernetes Service
Who we work with
Teams betting on AI, not experimenting with it
- Startups shipping their first AI product
- Enterprises modernising workflows with LLMs
- Founders validating an AI MVP before raising
- CTOs who need an architecture they can defend
Get in touch
Book a consult
Tell us what you're building and where it's stuck. We reply within one business day.