Embed GPT, Claude, and open-source language models directly into your workflows and applications. From RAG systems to fine-tuned models, we build AI that works in the real world.
End-to-end LLM solutions designed for reliability, speed, and cost efficiency at scale.
Retrieval-augmented generation with vector databases like Pinecone, Weaviate, and pgvector. Ground model responses in your proprietary data for accurate, hallucination-free output.
Custom model fine-tuning on your domain data. We prepare datasets, run training loops, evaluate performance, and deploy fine-tuned models that outperform generic prompts.
Systematic prompt design with chain-of-thought, few-shot examples, and guardrails. We build prompt pipelines that produce consistent, structured output every time.
Connect OpenAI, Anthropic, Cohere, and open-source models to your stack. We handle authentication, rate limiting, failover, streaming, and response caching.
Token budgeting, model routing, response caching, and tiered model strategies that cut your LLM costs by 40-70% without sacrificing quality.
Containerized deployments with monitoring, logging, A/B testing, and automatic scaling. Your LLM features ship stable and stay stable.
Real applications we have built for companies across industries.
Give employees instant answers from company wikis, SOPs, and documentation. Our RAG-powered search understands context, not just keywords, so teams find what they need in seconds instead of hours.
Upload contracts, reports, or research papers and ask questions in natural language. Extract clauses, summarize findings, and compare documents at scale with structured output.
Automate blog posts, product descriptions, email sequences, and marketing copy. Fine-tuned models match your brand voice while prompt guardrails ensure compliance and consistency.
Custom coding assistants trained on your codebase, conventions, and architecture. Accelerate development, automate code review, and generate tests aligned with your standards.
A proven four-phase approach to shipping LLM features that work.
We map your data landscape, identify the right model architecture, and define success metrics. You get a clear technical plan with cost projections before any code is written.
A working proof-of-concept in 2-3 weeks. We test model accuracy, latency, and edge cases with real data so you can evaluate results before committing to full build.
Production implementation with proper error handling, caching, monitoring, and cost controls. We optimize prompts, tune retrieval pipelines, and harden the system for scale.
Ship to production with observability dashboards, automated evaluations, and continuous improvement loops. We monitor output quality and iterate on prompts and retrieval as your data evolves.
Tell us about your use case and we will scope a solution with clear timelines and cost estimates.
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