Large language models are powerful generalists. We turn them into domain specialists: copilots that understand your product, assistants that call your APIs, and workflows that stay within policy. Custom LLM development here means architecture, evaluation, and product delivery — not just prompt files.
LLM product engineering for teams in India, USA, UAE, Germany, Australia and remote-first global buyers.
You need custom work when off-the-shelf chat cannot access private data, enforce roles, trigger tools, or meet latency and compliance requirements. We help you choose between prompting, RAG, fine-tuning, and agents — and we tell you when a simpler approach is enough.
Context assembly and memory, tool and function calling, structured outputs for downstream systems, streaming UX, multi-turn dialogue design, and admin controls for prompts and policies. Around that: auth, rate limits, audit trails, and dashboards for quality and spend.
RAG is usually the right first step for factual, changing knowledge. Fine-tuning helps with style, format, or specialised behaviour. Many production systems use both. We run the analysis so you do not pay for fine-tunes you do not need — or skip grounding when you do.
Define tasks the LLM must perform, failure modes that matter, and data it is allowed to see.
Build a gold set of prompts/answers. Measure quality before optimising models or prompts.
Add retrieval, tools, validation, and human review where risk is high.
Deploy with monitoring, versioned prompts, and a clear path to improve weekly.
We optimise for measurable task success, not model brand names.
LLM logic plus web/mobile UX, APIs, and ops in one engagement.
Data residency, retention, and access patterns considered early for EU and enterprise buyers.
It is building applications and workflows around large language models tailored to your domain — including prompts, retrieval, tools, fine-tuning where needed, UX, and production operations.
Almost never. Training a foundation model is rarely the right investment. We specialise in applying and adapting existing models safely and cost-effectively.
Yes. We support API-based models and private deployments (VPC/on-prem) when data residency or policy requires it.
Through caching, retrieval so prompts stay small, model routing, rate limits, and dashboards that show cost per workflow — not only tokens.
Share your use case and constraints. We will recommend architecture and a realistic pilot plan.
Book a free 30-minute consultation. We'll map the fastest path from concept to a production-ready product.