FAQs: Gen AI Development
Production LLM applications built to lastWe build full-stack Generative AI applications — RAG pipelines, LLM-powered features, fine-tuned models, and agentic workflows — from prototype to production.
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Common questions
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Which LLM provider do you recommend?
It depends on cost, privacy, and accuracy needs. We benchmark each candidate provider against your specific task.
What if our data contains sensitive information?
We deploy local or VPC-hosted models (Llama 3, Mistral) so sensitive data never leaves your controlled environment.
How do you measure quality of LLM outputs?
We build automated eval suites using LLM-as-judge techniques and ground-truth datasets specific to your domain.
How do you manage unpredictable LLM API costs?
Through response caching, prompt optimization, and matching model tier to actual quality requirements for each specific task.
Is prompt injection risk addressed?
Yes — through input sanitization, output filtering, and careful system prompt design specific to LLM-powered applications.
Should we use a hosted API or self-host an open-source model?
It depends on data sensitivity, cost at scale, and control needs — we evaluate this specifically for your unique situation.
How does this compare to hiring a full-time ML/AI engineer?
This project-scoped engagement is often more cost-effective for a defined project than continuous broad ongoing ML capacity.
Is client data and architecture kept confidential?
Yes — all data, prompts, and architecture details are treated as strictly confidential.
What's a common mistake companies make before seeking help?
Building an impressive demo, then discovering the gap to production-grade reliability and cost management is far larger than anticipated.
Can this handle very long documents or large context windows?
Yes — through intelligent chunking, hierarchical summarization, and retrieval strategies specifically tuned for long-context scenarios.
What happens if our model provider has an outage?
Applications are built with graceful degradation — fallback providers, cached responses, or clear messaging — rather than a complete outage.
How do you test a system with non-deterministic outputs?
Through statistical properties across many runs and semantic similarity checks, rather than exact string matching that would fail correct responses.
Is there a minimum project size for this service?
No — engagements range from a focused proof-of-concept to a full production deployment spanning multiple fully integrated features.
Is ongoing support available after launch?
Yes — ongoing monitoring ensures continued reliable performance as usage grows and underlying models continue to evolve.
Can this integrate with our existing customer support or CRM system?
Yes — GenAI features are commonly integrated directly into existing support, CRM, or internal tooling systems rather than requiring a standalone separate application.
Do you support both cloud and on-premise deployment?
Yes — deployment approach is chosen based on your actual data sensitivity and operational requirements, not a default assumption either way.
How long does a typical Gen AI development engagement take?
It varies considerably — a focused proof-of-concept may take a few weeks, while a full production feature build can take several months to fully mature.
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