Internal assistants
Ask questions about policies, products or past projects and get answers with sources.
Generative AI · LLM apps · RAG
Generative AI and RAG development services
A bigger model won’t fix wrong answers. Grounding it in your own documents and data will. We build GenAI assistants, search and document tools that answer from what you actually know.
Grounding beats a bigger model
Most GenAI disappointments come from the same place: the model is clever but doesn’t know your business, so it guesses. Retrieval-augmented generation (RAG) fixes that. Before the model answers, it looks up the relevant contracts, manuals, tickets or records, and it shows you where the answer came from.
We build the whole system around the model: getting the data in and keeping it fresh, chunking and search tuned for your documents, the right model for the job, access rules so people only see what they’re allowed to, and tests that catch bad answers before users do.
- Answers cite their sources
- Respects who can see what
- Large or small models, per task
- Tested before every release
What we build with generative AI
Semantic and hybrid search
Search that finds what people mean, even when they use different words from the document.
Document processing
Read contracts, invoices and forms, extract the fields that matter and check them.
Customer-facing GenAI
Help and support that sounds like your brand and sticks to the facts.
Small language models
Faster, cheaper and private models for focused tasks, run on your own infrastructure if needed.
Evaluation and guardrails
Test sets, answer scoring and filters, so you can see whether each release got better or worse.
Generative AI in production
Legal techRead the case study LogisticsRead the case study Field serviceRead the case study
LawSigna
AI contract lifecycle management that reads contracts, flags risky clauses and tracks every obligation and payment that follows.
11workflow stages
60-dayexpiry alerts
Formula2Ship
A team of AI agents runs order operations for a multi-courier shipping platform: picking couriers, chasing SLAs and answering customers.
22,000+orders a day, up from 5,000
3.2×monthly revenue in 3 months
Check’NdOut
An AI agent books jobs and builds crew schedules for a landscaping business that grew into a full field-service app.
3 → 1apps replaced
AIbooking and scheduling
How we work
- 1
Start with real questions
We collect the questions people actually ask and the documents that hold the answers.
- 2
Build the retrieval first
If the right passage isn’t found, no model can answer well. We get search right before tuning prompts.
- 3
Pick the model
Large models where reasoning matters, small ones where speed, cost or privacy matter more.
- 4
Measure, then release
We score answers against a test set and keep scoring after launch, so quality doesn’t drift.
Questions we often get
What is RAG?
Retrieval-augmented generation (RAG) is a way of making a language model answer from your own content. The system first searches your documents and data for relevant passages, then gives them to the model, which answers using that material and can cite where it came from.
Why not just fine-tune a model on our data?
Fine-tuning teaches a model style and patterns, but it’s a poor way to store facts that change. RAG keeps facts in your documents, so updating an answer means updating a document, and every answer can show its source. We sometimes use both.
Will our data be used to train public models?
No. We use enterprise APIs that don’t train on your data, or run open and small models inside your own cloud when data must stay in-house.
Should we use a large or small language model?
It depends on the task. Large models handle complex reasoning best. Small language models are faster, cheaper and easier to run privately, and they’re often good enough for focused jobs like classification or extraction.
How do you stop the AI from making things up?
Grounding answers in retrieved sources, telling the model to say when it doesn’t know, showing citations and testing answers against a set of known questions before every release.
What does a GenAI project cost?
It depends mostly on how much data there is, how messy it is and how many systems it lives in. After a free 30-minute call we send a plan and estimate, usually within a week.
Tell us what you’re building
A senior engineer will reply within one business day.