AI features with a defined job.
We don't build useless chat widgets. We integrate AI where it can dramatically improve a real business workflow, with clear boundaries around data privacy, evaluation, and human responsibility.
AI Trust Boundary
Retrieval-Augmented Generation (RAG)
Off-the-shelf AI models hallucinate because they lack your business context. We build RAG pipelines that force the AI to read your proprietary databases and documents before answering.
Vector Databases
We use Pinecone or pgvector to create searchable semantic indexes of your company data.
Deterministic Grounding
The model is strictly instructed: "If the answer is not in the provided documents, say 'I do not know'."
Proprietary Knowledge
Your data remains yours. By using RAG, the LLM acts only as a reasoning engine, not a storage engine.
Human-in-the-Loop Architecture
In highly regulated industries (Legal, Medical, Finance), AI cannot make final decisions autonomously. We engineer approval queues where AI acts as a drafter, and a human acts as the editor.
Zero Data Retention
We utilize enterprise APIs (like OpenAI Azure) with strict agreements that your data will not be used to train their base models.
Prompt Injection Defense
Input sanitization and output parsing layers to prevent malicious actors from jailbreaking the system.
On-Premise LLMs
For defense and healthcare, we can deploy open-weight models (Llama 3) entirely within your own VPC.