Software delivery
Software development after AI: review is the job now
AI writes the first draft. Code arrives faster and in bigger pieces, and review becomes the bottleneck. What changed on our team, and what I'd change tomorrow.
· Prafull B.
Applied AI engineering
The model is the easy part. The system around it is the job.
Qollabra builds AI systems that hold up in production, in domains where the rules are intricate and mistakes are expensive: source tracing, human review and recovery from failure, built in. We work with technology and services firms to deliver them for their clients.
Who we work with
Built for the people who have to make AI work.
Technology and services firms
Your clients want AI. We help you deliver it.
Demand for AI systems has moved faster than the people who can build them. We work with your team, under your brand, on the engagements your clients are asking for: document workflows, agents, and pilots that now have to run for real.
Teams in complex domains
AI that shows its working.
Finance, healthcare, insurance, legal, logistics, compliance: wherever one missed detail changes the answer. We build systems where every value is traced to its source and people review before anything becomes final.
What we do
Four things, done properly.
How we partner
Your client. Your brand. Our engineering.
For technology and services firms whose clients want AI systems their teams aren't yet staffed to build.
01
Inside your delivery team
Our senior engineers join your team on a client engagement, work to your process and under your brand. Your client relationship stays yours.
02
Architecture and review
We design the system with your team, then review the build as it goes: the pipeline, the evaluation, the permissions, the failure paths.
03
A defined build
We deliver an agreed system end to end, with documentation and a handover your team can run from.
Selected work
Where reliability is the requirement.
Legal technology · United States
AI-assisted analysis of large legal document sets
- Every extracted value linked back to its source page
- Specialists review and correct before results become final
- Document sets of thousands of pages processed in recoverable stages
What we believe
Built to run.
Not to demo.
The model is one component
Models and providers will change. What lasts is the system around them: data, evaluation, permissions, review, recovery and cost.
A suggestion is not a record
Model output stays a suggestion until a person or a rule confirms it. Human corrections are never silently overwritten.
Failure is designed in
Large jobs fail in parts. We build them to retry the part, not restart the whole, and to show people what actually happened.
Earn autonomy
We measure the simple version first. Agents get scoped tools, the user's permissions and approval before anything consequential.
Insights
Notes from production.
Evals
Product management for AI features: the test set is the spec
AI features don't fit strict test cases. What worked for us: real examples, good and bad answers, and a pass bar agreed with the client before building.
· Prafull B.
AI engineering
Prompt, context, loop engineering: why the names keep changing
Prompt engineering, then context engineering, now loop engineering. Each name came when the last one hit its limit. One skill stayed the same underneath.
· Prafull B.
Community
Qohort
A community of engineers who ship AI to production, hosted by Qollabra. It starts with a free weekend session: open Q&A on the problems demos skip.
Join a weekend sessionHave an AI system to deliver?
Tell us what your client needs, or what your team is stuck on. We reply within two working days.
Talk to us