Right-sized language models for the scale you actually run at
Model choice driven by latency, accuracy and cost per request, from small efficient models to private deployment where policy requires it.
2 wks
To a working POC
15
AI capabilities we build
9
Years in business
What you get
What every enterprise LLM project includes
For a business, the questions are data, access and cost. We settle them before a model is chosen.
Talk through the use caseModel selection justified against cost per request
Private or in-region deployment where policy requires it
Per-team budgets and spend monitoring
Our process
Four steps from policy to production
Governance first, then the model.
- 01
Agree data & access rules
What the model may see, who may ask, and where data is allowed to go.
- 02
Choose the hosting model
API, private cloud or self-hosted, compared on privacy, quality and cost.
- 03
Build the first use case
One department's workflow, connected to its systems with the right permissions.
- 04
Measure, govern & expand
Usage, quality and cost reviewed, then the next use case added.
Tech stack
What we build it with
Four layers, each chosen per project. Open one to see what sits in it and why.
Models & frameworks
The layer that generates. Chosen per task against quality, latency and cost.
Retrieval & data
Where grounded answers come from: your documents, indexed and tuned.
Serving & ops
How it runs in production, and how you see what it is doing.
Evaluation
How we know the output is good enough before a user sees it.
More AI capabilities
Fourteen more ways we apply AI
AI Software Development
End-to-end AI product builds.
AI Chatbot Development
Smart, conversational AI for business.
AI Agents Development
Linked agents for smarter tasks.
AI Voice Agents
Voice intake and call handling.
Generative AI Solutions
AI that creates and innovates.
LLM Chatbot Development
Grounded in your own content.
RAG Model Development
Smarter AI with search + generation.
GPT Integration
AI seamlessly in your workflow.
Prompt Engineering
Better prompts, better results.
Machine Learning Development
AI that learns from your data.
NLP Development
Classification, extraction, sentiment.
AI Workflow Automation
Process automation that holds up.
AI MVPs & POCs
Proof before you commit to a build.
AI Consulting
Where AI pays off, and where it doesn't.
Frequently asked questions
The questions we get asked about AI
Data handling, output quality, cost and ownership.
Start with one department
Two weeks, one use case, clear data rules and a costed plan for rolling it out.