LLMs, RAG pipelines, agentic workflows, and ML models — from prototype to production. We build intelligent systems that reason, retrieve, act, and scale.
We don't build AI demos — we build AI that runs in production, handles edge cases, and earns trust from the people who use it every day. Our teams have shipped enterprise RAG systems processing hundreds of thousands of documents, ML recommendation engines serving 160M+ annual users, and real-time inference pipelines that inform decisions in milliseconds.
Whether you're adding intelligence to an existing product or building an AI-native platform from scratch, we bring the engineering rigor that turns ambitious AI ideas into reliable, scalable systems.
GPT-4/5, Claude, and Amazon Bedrock wired into your product with proper guardrails, grounding, and cost optimization. Not a wrapper — a production system.
Retrieval-augmented generation with semantic chunking, vector embeddings, re-ranking, and citation tracking. Your data, made searchable and conversational.
Custom models for prediction, classification, and pattern recognition. From training pipeline to inference endpoint — deployed, monitored, and iterated.
Semantic search that understands intent, not just keywords. Recommendation engines that learn from behavior at scale.
Extract, classify, and structure information from documents at scale. OCR, form recognition, and intelligent processing for regulated industries.
Autonomous AI agents that plan, execute, and iterate. Multi-step reasoning with tool use, human-in-the-loop checkpoints, and orchestrated agent pipelines for complex business processes.
Context-aware chatbots, voice assistants, and speech-to-text pipelines backed by your proprietary data. Integrated into your workflows, not bolted on.
Traditional AI responds to prompts. Agentic AI plans multi-step workflows, uses tools, makes decisions, and executes autonomously — with human oversight at the checkpoints that matter. We've built agentic systems for pharmaceutical intelligence, legal automation, outbound sales, document generation, and production scheduling.
Discuss your use case →Agent decomposes the goal into sub-tasks, selects tools, and sequences execution steps
Calls APIs, queries databases, processes documents, runs calculations — autonomously
Assesses output quality, checks against constraints, decides whether to retry or escalate
Critical decisions surface for human approval before the agent proceeds — trust by design
Enterprise RAG pipeline for pharmaceutical intelligence. Multi-stage retrieval with semantic chunking, vector re-ranking, and grounding with citations. Bi-directional Salesforce integration.
CV parsing with Claude Vision (PDF, DOCX), structured skill extraction across 8 categories, and AI-powered candidate-to-JD matching with suitability scoring. Automated candidate presentation generation.
Automated extraction of invoice data from PDFs and images using Claude Vision. Multi-currency support, supplier auto-matching to projects or OPEX categories, and confidence scoring for verification workflows.
ML behavior pattern prediction for proactive wellbeing coaching. Custom models delivering real-time inference for personalized interventions. Used by F1 champions.
ML recommendation engine powering product discovery for 160M+ annual shoppers. High-performance ML modules integrated into a 5-year engineering partnership.
On-premise RAG pipeline for 115K+ engineering documents (PDF, DOCX, XLSX, PPTX, MSG). Custom extraction for 8 file types with OCR fallback, hybrid chunking (page-level + sentence-based), BGE-M3 embeddings, and Zvec vector storage. 24-experiment AutoRAG sweep optimizing chunk size, overlap, and search weights. Achieved 82% Recall@5 with dense-dominant hybrid search (0.7/0.3 weighting).
RAG-powered knowledge assistant searching 100K+ product reviews with semantic understanding. Context caching for sub-second responses. Fine-tuned models via Amazon Bedrock.
REST API backend serving thousands of users with AI-driven recommendations, experience analysis, and guest insights. ML pipeline for personalized travel experience matching using vector search and LLM orchestration.
Parameter-driven proposal and contract assembly for a global BPO company. AI pulls from curated knowledge bases (case studies, service descriptions, capability statements), enforces brand compliance, and generates tailored Word documents with section-level content control.
ElevenLabs-powered voice agent conducting outbound sales calls autonomously in 10 languages. Real-time HubSpot CRM sync, custom API orchestration for lead qualification, and automatic handoff to human agents via SmartTalk Dialer when live intervention is needed.
Agentic AI platform for legal document review, revision, and completion. Custom AI agents per client with proprietary document bases, semantic search across legal knowledge, and autonomous NDA lifecycle management — from drafting to counterparty negotiation.
Exclusive tech vendor for a funded B2B travel startup. Built the core API platform enabling travel brands to offer experiences to their guests, scaling to serve millions of requests. 12+ month ongoing partnership.
What we've learned building retrieval-augmented generation systems at scale — the patterns, pitfalls, and pragmatic choices from pharmaceutical intelligence to consumer product search.
Read more →What happens when you apply Karpathy's 'let the AI experiment overnight' pattern to retrieval-augmented generation? 24 experiments, 7 parameters, one clear winner.
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