Service · Generative AI
Generative AI Development
We turn model capability into a focused product: the right context, repeatable outputs, a usable interface and a clear quality bar.
Product strategy
Choose a problem where generation creates leverage
We identify where generated text, images, summaries or structured data can remove repetitive work or improve a decision without hiding the need for expert judgement.
- Document generation
- Summarisation and extraction
- Content operations
- Research assistance
- Structured data generation
- Multimodal workflows
Engineering
A model is one component of the system
The production layer handles context, templates, validation, permissions, versioning, cost and observability. We select models around the job and keep the architecture adaptable as capabilities change.
- Model selection and routing
- Prompt and context architecture
- Schema-constrained output
- Safety and policy checks
- Caching and cost control
- Provider abstraction
Quality
Define what good looks like before launch
We turn subjective expectations into review criteria and representative test cases. Automated checks and human review then reveal regressions before users do.
- Evaluation datasets
- Rubric-based review
- Adversarial testing
- Human approval flows
- Production feedback loops
- Model-change regression tests
Selected work
Evidence over adjectives.
Real operating problems shaped into products people can understand and use.
Common questions
Clear answers before we start.
Which AI model will you use?
We choose models after defining the task, quality bar, latency, privacy and cost constraints. The product architecture can support more than one provider where resilience or routing is useful.
Can generative AI produce structured data?
Yes. We use constrained schemas, validation and retries when the output must be consumed by software rather than only read by a person.
How do you reduce hallucinations?
The approach depends on the task and can include retrieval from approved sources, constrained output, verification steps, clear uncertainty behaviour and human review.
Start with the real workflow
Bring us the complicated part.
Tell us what your team is trying to improve, what information is available and where the current process breaks.
aryanchandwani@gmail.com