GENERATIVE AI SOLUTIONS
LLM systems that survive contact with real users.
RAG, agents, and AI-assisted workflows built with evaluation, access control, and cost ceilings designed in from the start — not retrofitted after the demo goes well and someone asks about compliance.
THE PROBLEM
A generative AI prototype takes a competent engineer about a week. Getting the same thing to work reliably across ten thousand documents, with the right people seeing the right subset, at a cost that doesn’t scale linearly with usage, takes considerably longer.
The gap between those two things is where most GenAI budgets disappear.
We build for the second one.
WHAT YOU GET
Retrieval systems
Over your documents, with access control that respects your existing permission model
Agent workflows
With explicit tool boundaries, human checkpoints, and audit trails
Evaluation harnesses
Automated test suites so you know when a change makes things worse
Cost and latency controls
Routing, caching, and model selection tuned to your actual usage
Guardrails and red-teaming
Against prompt injection, data leakage, and off-policy output
Production runbooks
Monitoring, alerting, rollback
HOW IT RUNS
Scope and evaluate. Define what “working” means numerically before writing code. Build the eval set first.
Build. Working system against real data, reviewed weekly.
Harden. Security review, load testing, cost tuning, guardrails.
Ship and hand over. Deploy, document, train your team.
ACCELERATORS USED
Junction for orchestration, Crew where the work is document-heavy, Assay for evaluation. Week one starts from working infrastructure rather than an empty repository.
WORKING WITH YOUR DATA
Working with your data. We work under NDA from day one, with read-only access scoped to what the engagement needs, revoked at project close. Where you’d rather nothing left your environment, we work inside it. Your data is never used to train models, ours or anyone else’s. Happy to walk through specifics on a call.
FAQ
Which models do you use?
Whichever fits the requirement and your constraints. We build model-agnostic so you’re not locked to a vendor whose pricing or terms may change. For most enterprise work that means a routing layer across two or three providers plus the option to run open-weight models in your own environment.
Can this run entirely inside our infrastructure?
Yes. Self-hosted open-weight models and fully private deployments are supported, with a cost and capability tradeoff we’ll walk you through honestly.
How do you stop it making things up?
You don’t stop it entirely; you measure it and constrain it. Grounded retrieval with citation, refusal behavior when confidence is low, and an evaluation suite that catches regression. Anyone promising zero hallucination is selling something.
What about prompt injection?
Treated as a standard threat model. Untrusted content is never given instruction authority, tool access is explicitly scoped, and anything with side effects requires confirmation.
What does this cost to run?
We model it before we build it and design to a ceiling you set. Runtime cost is a design constraint in our work, not a surprise in month three.
DATABRIDGECRUX
Tell us what’s stuck.
Most of our engagements start with a 30-minute call where we listen more than we pitch. If we’re not the right fit, we’ll say so and point you somewhere better.