Language models, turned into real features.
Every build is grounded in your data and measured with evals, so outputs are accurate, safe, and shippable.
LLM app development
Custom apps and features powered by large language models, built into your product.
RAG & knowledge grounding
Retrieval over your documents and data so answers are accurate, sourced, and current.
Content & creative generation
Engines that draft copy, emails, and creative at scale, on-brand and on-message.
Copilots & assistants
In-product copilots that help your users and team get work done faster.
Fine-tuning & adaptation
We adapt open and hosted models to your domain, tone, and tasks when it pays off.
Prompt engineering & evals
Systematic prompting plus automated evaluations that keep quality high in production.
Put generative AI where the writing and lookup lives.
Content at scale
Blog, product, and marketing copy generated and reviewed in a fraction of the time.
Support copilots
Draft accurate replies grounded in your help center and policies.
Code assistants
Internal copilots that understand your codebase and conventions.
Knowledge search
Natural-language search across your docs, wikis, and tickets.
Summarize & extract
Turn long documents and calls into structured summaries and fields.
Marketing generation
Personalized campaigns and variants produced and tested automatically.
From prompt to production.
We ground, evaluate, and harden every generative feature before it reaches your users.
Discover & scope
Find the use-cases where generative AI creates real, measurable leverage.
Design & ground
Architect retrieval, prompts, and data grounding, then a proof-of-concept.
Build & fine-tune
Ship the feature into your product, fine-tuning models where it helps.
Evaluate & deploy
Automated evals, guardrails, and monitoring, then a staged rollout.
The right model for each job.
We choose the model, framework, and vector store that fit your accuracy, latency, and cost targets, and swap them as the frontier moves.
Generative AI that holds up in production.
- Grounded in your data. Retrieval keeps answers accurate and sourced, no confident nonsense.
- Evals, not vibes. We measure quality against real test sets before anything ships.
- Tuned for cost & latency. We engineer for the response times and budgets your product needs.
- Your data stays yours. Architected for privacy, and deployable inside your own environment.
Generative AI, answered.
What can generative AI actually do for us?
Anything that involves generating or understanding language: drafting content, answering questions from your docs, summarizing, extracting data, or powering in-product copilots. We start by finding the use-cases with the clearest ROI.
How do you prevent hallucinations?
We ground models in your data with retrieval (RAG), constrain outputs, and run automated evals against known-good answers. Risky actions are gated behind guardrails and human review.
Do you fine-tune models or use RAG?
Usually RAG first. It's faster, cheaper, and easier to keep current. We fine-tune when you need a specific tone, format, or task performance that prompting alone can't reach.
Which model do you use?
We're model-agnostic: OpenAI, Anthropic, Gemini, or open models like Llama. We pick per use-case on accuracy, latency, and cost, and keep the option to switch.
How fast can we launch a feature?
A grounded proof-of-concept typically lands in 2–3 weeks, with a production feature in around six, depending on integrations and compliance needs.
Ready to build with generative AI?
Tell us what you'd like to generate, answer, or automate. We'll come back with a scoped plan and a timeline.