How do you keep AI costs under control?

Aggressive caching, intelligent model routing (use cheaper models for simple tasks, expensive ones for complex), prompt optimization, and batch processing. We have reduced monthly API bills from $2,000 to $400 for clients without quality loss.

Can you work with our private/sensitive data?

Yes. We deploy models locally or use enterprise API tiers with data processing agreements. For highly sensitive data, we can run open-source models on your own infrastructure with zero data leaving your network.

What happens when the AI gives wrong answers?

Every system we build has guardrails: confidence scoring, human-in-the-loop review for low-confidence outputs, source citations for RAG systems, and fallback paths. We design for graceful degradation, not blind trust.

AI that solves real problems, not just demos well.

We build production AI features — from RAG-powered search to automated document processing to intelligent agents — that run reliably at scale, not just in a Jupyter notebook.

What We Deliver

Capabilities

LLM integration & fine-tuning

OpenAI, Claude, and open-source model integration with prompt engineering, output parsing, guardrails, and cost optimization. We have cut API costs by 60% through smart caching and model routing.

RAG & knowledge systems

Retrieval-augmented generation pipelines using vector databases, document chunking, embedding strategies, and re-ranking. Your AI answers questions from your actual data.

Intelligent agents & workflows

Multi-step AI agents that can research, analyze, and act. Automated workflows for document processing, data extraction, content generation, and decision support.

Computer vision & image AI

Image classification, object detection, OCR, and visual analysis. From quality inspection in manufacturing to receipt scanning in expense apps.

Tech Stack

Technologies we use

OpenAI GPT-4oClaudeLangChainPineconePythonTensorFlowHugging FaceChromaDB

How We Work

Our process

Problem validation

We assess whether AI is the right solution for your problem. Sometimes a well-designed rule engine beats a neural network. We will tell you honestly.

Proof of concept

A working prototype in 1-2 weeks that proves the approach works with your actual data. No month-long research phases before you see results.

Production pipeline

We build the full pipeline: data ingestion, model orchestration, error handling, fallbacks, monitoring, and cost tracking. AI that works at demo scale and production scale.

Monitoring & iteration

Response quality tracking, cost dashboards, latency monitoring, and continuous prompt improvement based on real user interactions.

FAQ

Common questions

Get Started

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