AI that does real work inside your product
We integrate LLMs and AI services where they create measurable value — with clear data boundaries, evaluation and cost control.
AI Solutions
What we build
- 01
LLM integrations
Language models connected to your product, data and workflows.
- 02
AI assistants
Assistants that answer from your documents and knowledge base.
- 03
Intelligent automation
Classification, extraction and routing of documents, emails and requests.
- 04
Data-driven features
Search, recommendations and summaries built on your own data.
Typical use cases
- Support and knowledge assistants
- Document processing
- Lead and request qualification
- Internal search
- Content generation workflows
- Reporting and summaries
Our approach
- 01
Start with the use case
We define where AI saves time or money before choosing a model.
- 02
Your data, your boundaries
Clear rules for what data is sent where, and how it's stored.
- 03
Evaluate, then ship
Test sets and quality checks so behavior is predictable, not a demo.
- 04
Cost under control
Model choice, caching and limits planned for production usage.
Technology
The stack depends on the project.
Typical choices — the final stack is defined during discovery.
- Models
- OpenAILLM APIsOpen-source models
- Engineering
- PythonNode.jsTypeScript
- Retrieval
- Vector searchPostgreSQL / pgvectorEmbeddings
- Infrastructure
- AWSGoogle CloudAzureDocker
Process
Process
- 01
Discovery
We understand the business, users, goals and technical requirements.
- 02
Strategy & Estimate
We define scope, architecture, timeline and estimated budget.
- 03
Team
We assemble the right specialists for the project.
- 04
Development
The product is built in structured development cycles.
- 05
QA
Testing, bug fixing, performance and quality control.
- 06
Launch
Deployment, release and transition to ongoing development.
FAQ
Is our data safe if we use LLMs?
We design data flows explicitly: what is sent to a model, which provider is used, and whether data is retained. Sensitive data can be masked or kept on your infrastructure.
Can AI be added to our existing product?
Yes. Most AI features are integrations into an existing product through its backend and data.
How do you know the AI works correctly?
We build evaluation sets from real examples and check quality before and after each change.
Start here
Exploring AI for your product?
Describe the task you want to automate or improve. We'll assess where AI fits and where it doesn't.
What happens next
- 1
We review your request
And come back with clarifying questions.
- 2
Intro call
We discuss goals, users, constraints and timing.
- 3
Scope & estimate
You get a proposed scope, team, timeline and budget.