Bengaluru, India
AI systems that hold up in production
Most AI work stalls somewhere between the demo and the deployment. We build the part that has to survive real users, real data, and the day somebody asks why the system said what it said.
What we do
Three practices- 01
Applied AI systems
Systems where a model does real work inside a business process rather than sitting next to it in a chat window. We choose the model, build the evaluation harness that proves it is good enough for the job in front of it, and put deterministic guardrails around the parts that cannot be left to inference.
- Model selection and evaluation
- Retrieval and grounding
- Deterministic guardrails
- 02
Agentic automation
Workflows that run without someone watching them. The agent takes a task, calls the tools it needs, and hands back a result a person can check. Most of the effort goes into the failure paths, because that is where these systems actually break.
- Tool and API orchestration
- Human review gates
- Recovery and retry logic
- 03
Full stack product build
The model is a fraction of the work. Data pipelines, auth, the interface people use every day, deployment on your infrastructure, and the monitoring that tells you when output quality moves. We build all of it, not just the interesting part.
- Interfaces and dashboards
- Pipelines and storage
- Deployment and monitoring
Where we go deep
Three domainsDomain knowledge is what decides whether an AI system survives contact with the actual work. These are the three areas where we already know the vocabulary, the edge cases, and what the people using it are accountable for.
- Architecture, engineering, construction
AEC
Drawings, specifications, tenders and site records. The material is unstructured, inconsistent between projects, and full of references that only make sense to somebody who has done the job. We build systems that hold those relationships instead of flattening them, and that produce output an engineer is willing to sign.
- Drawing and specification understanding
- Quantity and cost workflows
- Approval and compliance trails
- Enterprise resource planning
Agentic ERP
Most of the rules that actually run an ERP are not in the ERP. They sit with the person who has approved the same exception for eleven years. Agents are useful here only if they can be constrained, audited and stopped, so that is how we build them.
- Cross module workflows
- Approvals and exception handling
- Master data reconciliation
- Financial services
Fintech
Reconciliation, onboarding, risk and reporting, where a wrong answer costs real money and somebody may later ask how it was produced. Inference stays away from arithmetic. Every decision the system makes leaves a trail a person can follow.
- Reconciliation and settlement
- Onboarding and verification
- Auditable decision trails
Work outside these three is welcome, it just starts with more questions from us. If we do not know your domain well enough to be useful, we would rather say so than learn it on your budget.
How we orchestrate
Four stepsThe same four steps on every project. The order matters more than it looks, because most failed AI work skips the second one.
- 01
Understand the problem
We start with the process, not the technology. Where the work gets stuck, who is accountable for the output, and what a good result looks like to the person who has to sign it. Most of what decides whether a project is worth doing at all comes out of this step.
OutputProblem statement
- 02
Find where agents earn their place
Not every gap is an AI problem. We work out which parts of the process an agentic system genuinely improves, either by removing work nobody should be doing or by freeing people for the parts that need judgement. If a script and a form would do the job, we will tell you that instead.
OutputScope, and what we ruled out
- 03
Test with a human in the loop
The system runs against real cases while your team reviews what comes out, and corrections go straight back in. The review gate stays up until the numbers say it can come down, and on the steps where a wrong answer is expensive it stays up for good.
OutputEvaluation results
- 04
Deploy and monitor
Going live is the start of the work. We watch output quality, cost and the failure paths after release, because that is where these systems drift, and drift usually reaches a customer before it reaches a dashboard.
OutputRunning system, under watch
Who
BengaluruWe are a small engineering company. The people who scope your project are the people who write the code.
Small is a decision, not a stage we are trying to grow out of. It means we take on few projects, we know every part of the systems we ship, and there is nobody in the middle translating between you and the engineer. If we think a problem is a poor fit for us, we will say so early rather than take the work.
Contact
Tell us what you are trying to build.
If you have a problem and no idea whether AI is the right tool for it, that is a fine place to start. We will tell you if it is not.