LLM / RAG / AGENTS
AI shipped to real users — not another prototype
The demo is the easy part. We build the systems that survive contact with production: grounded, monitored, cost-controlled and integrated with the tools your team already uses.
The problem
The gap between a demo and production is where AI dies
A convincing prototype hides the hard parts: grounding answers in your real data, controlling inference cost, handling failure, integrating with legacy systems, and keeping autonomous actions auditable. Orchestration complexity — not model quality — is what usually stops a build from crossing into production. We engineer for that gap from day one.
What's included
- 01
LLM integration & model orchestration
Features embedded into live apps and back-office systems, with provider routing, failover and cost controls.
- 02
RAG & knowledge assistants
Retrieval-grounded answers from your approved data, with source citations and guardrails against hallucination.
- 03
Custom agents & workflow automation
Scoped single-purpose and multi-agent workflows with human-in-the-loop approval gates on consequential actions.
- 04
Secure deployment
Architecture that fits your risk profile, including on-prem and air-gapped options for regulated data.
- 05
Monitoring & audit trails
Observability, cost tracking and audit logging so you can see — and trust — what the system is doing.
What you get out of it
- AI features in production with measurable cycle-time and productivity gains
- Grounded, citable answers instead of confident hallucinations
- Controlled inference cost and auditable, secure deployment
How we work
The Qanz Loop, applied here.
- 01
Position
Decide what's worth winning
We start from the business problem, not a channel or a model. Positioning, audience, the use cases worth funding, and the data and governance reality behind them — so everything downstream points the same way.
- 02
Build
Ship the system, not a slide deck
Senior people build the working thing: campaigns wired to first-party signals, content and creative systems, or AI features that reach production. Scoped tightly, with human review where it matters.
- 03
Compound
Make the gains stack
We measure what's incremental, fix what isn't, and stay engaged after go-live. Visibility, pipeline and capability are meant to grow without resetting every time a budget pauses.
Data first, model second
The reliable pattern is to fix the data foundation and set governance before choosing a model; the failure pattern is the reverse. We sequence builds that way, scope agents tightly to a measurable process rather than open-ended autonomy, and stay engaged after go-live to own the result — not hand over a repo and disappear.
Questions
Worth asking.
Can you work with our existing stack and data?
Yes — that's the point. We integrate with the systems and data you already run, and design the deployment around your security and compliance needs, including air-gapped options.
How do you stop the AI from making things up?
Retrieval grounding against your approved sources, citations on answers, evaluation harnesses, and human-in-the-loop gates where an action carries consequences. Accuracy is engineered, not hoped for.
Who owns what you build?
You do. We build on your infrastructure and hand over working software, documentation and the capability to run it — covered explicitly in the engagement terms.
More services
Often paired with this.
Start the conversation
Let's scope it — honestly.
Tell us the problem you're actually trying to solve. We'll tell you whether it's marketing, AI, or both — and whether we're the right people for it.