Which AI models do you work with?

We are model-agnostic. We regularly ship on Anthropic Claude, OpenAI GPT, and open-weight models like Llama, and we design the integration layer so you can switch or mix providers as pricing and quality evolve.

How do you keep our data private?

We default to API providers with zero-retention agreements, keep sensitive data out of prompts where possible, and can deploy open-weight models inside your own cloud when data cannot leave your environment.

How do you control AI costs?

Every engagement includes a token budget, caching strategy, and model-tiering plan (small models for cheap steps, frontier models only where quality demands it). You see projected unit economics before we build.

AI features that survive production, not demos that die in a notebook.

Most AI projects stall between the prototype and production. We build the unglamorous parts that make AI dependable: evaluation suites, retrieval pipelines, guardrails, observability, and cost budgets, so the feature your users see keeps working after launch.

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What we deliver

AI & Machine Learning, delivered as working software

LLM product features

Copilots, assistants, document intelligence, and workflow automation built on Claude, GPT, or open-weight models, with structured outputs and fallbacks.

RAG & search pipelines

Retrieval systems over your documents and data: chunking strategy, embeddings, hybrid search, reranking, and citation-grade answers.

Evaluation & guardrails

Automated eval suites, red-team test sets, prompt regression testing, and content guardrails so quality is measured, not assumed.

ML & data systems

Forecasting, classification, and recommendation models with the data pipelines and monitoring they need to stay accurate.

How we work on ai & machine learning projects

Evaluation-first: every AI behavior has a test suite before it ships
Token and latency budgets tracked from the first sprint
Model-agnostic architecture that lets you swap providers without a rewrite
Human-in-the-loop and fallback paths for the failure cases

Technologies we reach for

Chosen for reliability and hireability, so your future team will thank you.

Claude API
OpenAI API
LangGraph
pgvector
Pinecone
Python
TypeScript
Vercel AI SDK

See these stacks at work in our case studies

Engagement

Four steps from first call to production

01
Creative Discovery
Creative direction and technical architecture blueprint.
02
Prototyping & Motion
Interactive motion prototypes and design system.
03
System Engineering
Deployed production environment with core features.
04
Optimization & Launch
Live cinematic platform and performance reports.

Proof

SaaS · B2B software company

Deflecting 62% of support tickets with a grounded AI assistant

A retrieval-grounded AI support assistant that resolves 62% of inbound tickets with answers cited from the client's own documentation.

Read case study

Fintech · Consumer lending fintech

Cutting loan decisions from 4 days to 7 minutes

A digital lending platform with automated underwriting that took loan decisions from a 4-day manual review to a 7-minute automated flow.

Read case study

FAQ

AI & Machine Learning: common questions

Specific answers for this service. For pricing, ownership, and process questions, see the FAQ on our homepage.

Ready to scope your ai & machine learning project?

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