Focus Area
AI Integration
Automate the work that slows your team down. Private, on-premise options available for industries where data security is non-negotiable.
What We Do
AI that works with your actual business
Most AI projects fail because they start from the tool instead of the workflow. We start by understanding how work actually moves through your team, then build systems around that - not off-the-shelf products bolted on top.
- Workflow mapping before any build
- LLM integration into existing tools and processes
- Data privacy and compliance review
- Agentic workflows for repetitive tasks
- Custom fine-tuning and RAG pipelines
- Team training and handoff documentation
Use Cases
Document Processing
Extract data, summarize reports, classify documents at scale without manual review.
Internal Knowledge Search
Let your team ask questions and get answers from your own docs, policies and data.
Customer Support Automation
Triage tickets, draft responses and resolve common issues automatically.
Developer Productivity
Agentic coding tools, automated code review and CI/CD integrations.
Need data to stay on your servers?
We specialize in private, on-premise AI deployments using local models (Ollama, Llama, Mistral) running on your own hardware. Nothing sent to OpenAI or any third-party cloud. Designed for healthcare, legal, finance and any business where data privacy is critical.
See Enterprise optionsFAQ
Common questions about AI integration
What kinds of workflows can AI actually automate?
Document processing, customer support triage, data extraction, internal knowledge search, report generation, code review and more. The best place to start is with tasks your team does repeatedly that follow a consistent pattern.
Do we need to share our data with OpenAI or other cloud AI providers?
Not necessarily. We can run AI models entirely on your own infrastructure - nothing leaves your network. This is critical for legal, healthcare, finance and other regulated industries.
How long does an AI integration project take?
It depends on scope. A focused automation for a single workflow can be live in 2 to 4 weeks. A broader rollout across multiple teams and systems typically takes 2 to 3 months.
What is RAG and do we need it?
RAG (Retrieval-Augmented Generation) lets AI answer questions using your own documents and data instead of relying on general training knowledge. If your team needs AI to work with internal policies, product docs or customer data - you need RAG.
How do engagements usually start?
With a conversation about your workflows, where the friction actually is and what is realistic. We would rather tell you something is a bad fit early than build it.
Do you train our team after the build?
Yes - work includes team training and handoff documentation so your staff can use and maintain what gets built without depending on us forever.
Working on something in this space?
We are always up for a conversation about agents, retrieval or running models privately - whether or not it turns into work.
Get in Touch