AI Development Company

Yes. Our AI engineering retainer gives you on-call senior engineers and designers for active builds. Most retainers include same-day response, weekly sprints, prompt and evaluation help, and direct Slack access to the team.

Absolutely. That's exactly who we help most. You bring the vision. We handle strategy, design, prompt engineering, model selection, LLM implementation, backend, integrations, and deployment. You don't need to write Python or understand RAG.

AI development pricing depends on scope, data complexity, and engagement model. We offer fixed-cost projects, nearshore teams, dedicated teams, and custom quotations. Audits and rescue work start from a few hours of senior time. Full AI MVPs are scoped per project. Book a call for a tailored quote within 24 hours.

Four primary models: Fixed Cost Projects for well-defined scope, Nearshore Teams for time-zone-aligned delivery, Dedicated Teams for long-term product investment, and Custom Quotations for complex or regulated engagements. We'll recommend the right fit during discovery.

Most AI rescues cost overruns, latency problems, hallucinations ship within 24 to 72 hours of kickoff. Complex rescue work can take longer, but we always share a realistic timeline upfront.

Yes, when done right. Production AI means evaluation, guardrails, observability, grounding, and cost controls built in from day one which is exactly what we handle. We don't ship demos dressed up as products.

Yes. Our senior engineers extend, customize, and harden AI codebases using traditional development workflows. You're never limited to what a platform or single model can do out of the box

All of them. We select the right model per project based on cost, latency, quality, and privacy. Many products use multi-model architectures GPT-4o for reasoning, Claude for long-context, open-source for high-volume tasks.

Reliability is a first-class concern. We build evaluation suites, retrieval grounding, structured outputs, guardrails, and fallback logic into every AI product. Quality is measured objectively before launch, during evaluation, and in production.

Yes. We design end-to-end RAG pipelines ingestion, chunking, embeddings, vector storage, hybrid retrieval, re-ranking, and generation over documents, databases, CRMs, and internal knowledge, with enterprise security and access controls.