AI that does real work inside your business
We design and build AI agents, assistants and automations that connect to your data and systems, so teams spend less time on repetitive work and more on decisions.
What we build with AI
Each engagement starts from a business process, not a model. We pick the simplest approach that is reliable enough to run every day.
AI-powered business automation
Automate approvals, data entry, triage and follow-ups by combining AI with rule-based workflows and your existing tools.
AI agents
Task-focused agents that can look things up, call your APIs and hand off to a person when a step needs human judgement.
Retrieval-Augmented Generation (RAG)
Assistants that answer from your own documents, policies and knowledge bases, with sources your team can check.
LLM integrations
Add language-model features to your product or internal tools through well-tested APIs, with cost, latency and privacy controls.
Intelligent chatbots
Customer and employee assistants for websites, WhatsApp and internal portals that hand over to your team cleanly.
Document processing
Extract, classify and validate information from invoices, contracts, forms and scanned documents into structured data.
AI for business intelligence
Summaries, anomaly flags and natural-language questions on top of your reporting data.
AI workflow automation
Multi-step pipelines that move work between systems, with logging and review points built in.
Custom AI applications
End-to-end AI products, from prototype to production, with evaluation, monitoring and a maintainable codebase.
How we keep AI dependable
- Grounded in your data
- Answers come from approved sources, and we design for “I don’t know” rather than guesses.
- People stay in control
- Review steps, permissions and audit logs are part of the design from the first sprint.
- Measured before launch
- We test against real examples from your business so quality is checked, not assumed.
- Built to be maintained
- Clean integrations and documentation, so models and prompts can be changed without a rebuild.
How a project runs
- Step 1
Find the right use case
We map the process, data and success criteria, and agree where AI genuinely helps.
- Step 2
Prototype on real data
A working proof of concept tested against your own examples.
- Step 3
Build for production
Integrations, security, guardrails, evaluation and monitoring.
- Step 4
Launch and improve
Roll out to users, track quality and costs, and refine.
Tools we work with
- Models & frameworks
- OpenAIVercel AI SDKLangChainPython
- Data
- PostgreSQLVector searchREST & webhooks
- Delivery
- Next.jsNode.jsDockerAWS
Common questions
Can AI work with our existing software?
Usually, yes. We connect through APIs, databases or exports from your current tools, and only suggest new systems when integration is not practical.
How do you handle data privacy?
We agree what data the AI may use, keep sensitive data out of prompts where possible, and choose deployment options that match your compliance needs.
How long does a first project take?
It depends on scope and data readiness. We start with a short discovery and give you a plan with milestones before any build begins.
Often paired with
Have a process that should run itself?
Tell us how it works today. We will tell you honestly whether AI is the right fix.