AI Development
Most "AI features" are a chat widget bolted onto an existing product. We build AI that's wired into your actual data and workflows — chatbots grounded in your knowledge base, agents that call your internal APIs, models trained on your own numbers instead of a generic benchmark.
What we build
Wired into your systems, not bolted on.
AI chatbots
Support and sales chatbots grounded in your actual documentation and data, not a generic prompt wrapper.
Generative AI applications
Content, code, and document generation tools built around your workflow, with human review where the output matters.
AI agents
Multi-step agents that call your internal APIs and tools, with logging you can audit after the fact.
Predictive analytics
Forecasting and anomaly detection models trained on your historical data, not a vendor's generic benchmark.
Automation software
Workflow automation that replaces manual steps in operations, not the operations team.
NLP & speech recognition
Document processing, transcription, and speech recognition pipelines tuned for domain-specific vocabulary.
AI-integrated CRM & ERP
AI features embedded into the CRM or ERP you already run, not a separate tool nobody opens.
How we engage
The same process, every time.
Step 1
Scope
We read what exists — code, requirements, or both — and quote a number in writing before any contract is signed.
Step 2
Build
A fixed team scopes and ships it. Weekly demos, no unannounced handoffs mid-project.
Step 3
Handoff
Documentation, a defined support window, and a codebase your own team can actually maintain.
Stack
What we build it with.
- Models & frameworks
- OpenAI, Anthropic, LangChain, LlamaIndex
- Machine learning
- TensorFlow, PyTorch, scikit-learn
- Retrieval infrastructure
- pgvector, Pinecone, Kubernetes, Docker
- Data
- PostgreSQL, Redis, Airflow
AI is only as reliable as the process behind it. We'll tell you where a deterministic system beats a model before we build either.