Retrieval that cites its sources
Search and copilots that answer from your own material and point at the passage they used, so a reviewer can check the claim instead of trusting it.
Python · pgvector · evaluation sets
AI engineering · Surat, India
AI that still works six months after launch.
Most AI projects die after the demo, not during it. Retrieval quality slips. Nobody owns the evaluation set. Costs creep. Eventually the team goes back to the spreadsheet. We build the system and the operating discipline that keeps it alive.
What we build
Four kinds of work
Agents that finish the job
Tool-using systems that carry a task through the software you already run. Where being wrong is expensive, a person approves before anything commits.
Orchestration · tool APIs · audit logs
Models that survive your data
Forecasting, scoring, inspection and OCR built against what you actually have. Missing fields, bad scans, drift. Not a clean benchmark set.
Training · monitoring · retraining
The product around the model
Interfaces, APIs, workflow logic. This is usually what decides whether a working model becomes something the team opens every morning.
Next.js · FastAPI · cloud deployment
The mechanism
A model reads a sentence by weighing it against itself.
Point at a word. The arcs are the other words that change its meaning, which is what a transformer computes on every token. Getting those weights right on your data is most of the job.
Trusted by product teams shipping real AI
Proof of delivery
Numbers behind the work, not around it.
Five years of shipping AI under engineering discipline. Every figure here maps to a system that is still running.
- 45+
- AI systems shipped
- 25+
- Countries served
- 12 weeks
- Median time to production
- 4
- Layers we cover
Copilots, retrieval search, agent workflows and vision pipelines, all running in real environments.
Remote-first delivery across five timezones.
From kickoff to the first real traffic through the system.
Strategy, design, engineering, and support after launch.
Selected work
Real products, complex systems, and AI that feels expensive.
From lead intelligence to voice AI and automation platforms, the work has to prove SoftUs can handle serious product and engineering pressure.
