Service / AI Development

AI that solves
real problems.

We design custom AI models, fine-tune frontier LLMs to your data, and build production-grade AI systems for teams that need more than a generic chatbot. From private deployments to retrieval and agents, we make AI work where it matters.

What We Build

What We Build

End-to-end AI systems — model, retrieval, evaluation, and deployment — tuned to your problem and your data.

Custom AI Models

Models built for your domain — trained, distilled, or composed from open-source foundations.

  • Domain-specific model design
  • Open-weight fine-tunes (Llama, Mistral, Qwen)
  • Distillation & smaller specialist models
  • Private deployment on your infrastructure

LLM Fine-Tuning

Adapt frontier models to your data, tone, and judgment — with rigorous evaluation at every step.

  • Supervised fine-tuning (SFT) & DPO
  • Instruction & preference datasets
  • Eval harness design & regression testing
  • Cost vs. quality tradeoff analysis

RAG & Retrieval

Wire your AI into the knowledge it actually needs — your docs, your data, your truth.

  • Vector & hybrid search pipelines
  • Document chunking & embedding strategy
  • Query rewriting & re-ranking
  • Citation & grounding for trust

AI Agents & Automation

Multi-step agents that take action — drafting, deciding, and executing inside your workflows.

  • Tool-using agents & function calling
  • Long-horizon task planning
  • Human-in-the-loop checkpoints
  • Workflow integration with existing systems

AI Integrations

Bring AI into the products and tools your team already uses — without the vendor lock-in.

  • Provider-agnostic abstractions
  • Streaming, batching, & cost controls
  • Identity, audit, & access policy
  • OpenAI, Anthropic, Google, open-source

MLOps & Guardrails

Production AI you can actually trust — monitored, evaluated, and safe by design.

  • Online evals & drift detection
  • Prompt injection & safety guardrails
  • Cost, latency, & quality dashboards
  • Versioning, rollback, & A/B testing
How We Deliver

Our AI Process

We start from your problem, not the model — and only ship what we can measure.

Use Case & Feasibility

We map the business problem, identify where AI helps versus where it doesn't, and de-risk early with a clear evaluation plan.

Data & Eval Setup

We assess data readiness, build labeled eval sets, and define the metrics you'll judge the model on before any training begins.

Modeling & Iteration

Prompt, fine-tune, or train — we pick the simplest approach that meets the bar and iterate against your eval set.

Deployment & Monitoring

We ship to production with guardrails, observability, and a feedback loop so the system gets better with use.

Stack

Models & Tools We Use

Provider-agnostic by default. We pick the right model for the job — proprietary, open, or somewhere in between.

— Foundation Models
  • · OpenAI (GPT, o-series)
  • · Anthropic Claude
  • · Google Gemini
  • · Meta Llama
  • · Mistral
  • · Qwen & DeepSeek
— Frameworks & Tooling
  • · PyTorch
  • · Hugging Face
  • · LangChain & LlamaIndex
  • · DSPy
  • · vLLM & Ollama
  • · OpenAI Evals
— Vector & Data
  • · Pinecone
  • · Weaviate
  • · Qdrant
  • · pgvector
  • · Postgres & S3
  • · DuckDB & Parquet
— Deployment & Ops
  • · AWS Bedrock
  • · Azure OpenAI
  • · Modal & Replicate
  • · RunPod
  • · Hugging Face Inference
  • · Docker & Kubernetes
Let's Build

Ready to put AI to work?

Tell us the problem you're trying to solve. We'll tell you whether AI is the right tool — and how we'd build it if it is.