Ship the AI Feature Already on Your Roadmap

Not in the systems we build. Our retrieval-based approach grounds every answer strictly in your retrieved document content, with an explicit "no answer found" response when the available context doesn't support one — never a best-effort guess. That's the behavior running today in a production document-search system we built for a mid-size software company.

The workshop runs 1–2 weeks, fixed cost. You walk away with an architecture decision document and an integration roadmap — real deliverables you can act on even if you don't build with us. If you move forward, the build phase starts from exactly what the workshop scoped, priced time-and-materials with a not-to-exceed ceiling.

The first 1–2 weeks are the AI Capability Workshop — an architecture review against your real codebase and data, ending in a roadmap and a cost ceiling you approve. The rest of the first month moves into build: the first architecture decisions get made against your actual data, with your technical champion reviewing and able to redirect at each checkpoint, not waiting until the end to see anything.

Two phases. First, a fixed-cost AI Capability Workshop — 1–2 weeks — that scopes the real integration and produces an architecture decision document and roadmap. Second, if you proceed, the build runs time-and-materials with a not-to-exceed ceiling set from what the workshop found, not quoted blind upfront.

Five things move the number: how large and complex your codebase is, how many systems the integration has to touch, how structured your data already is, whether compliance requirements like SOC2 or HIPAA-type constraints apply, and whether the integration needs to run in real time or can run in batch. The workshop prices these against your specific system instead of guessing.

The workshop is built to answer that before you commit to a build. You get an architecture decision document and roadmap for $3,500–$6,000 regardless of whether you proceed — so the first money you spend buys a clear answer, not a sunk-cost build. Compare that to hiring: recruiting, compensating, and ramping an AI engineer internally typically takes 2–4 months before any code ships, with no roadmap deliverable if it doesn't work out. Once it's live, the categories worth tracking are usually manual effort reduced, response or processing time, user adoption, and answer accuracy — track whichever of those maps to your actual workflow, not a generic dashboard metric.

You won't get handed off to a different team once the contract's signed. The team that scopes your workshop is the team that builds and supports your integration afterward, and 90% of our engineers stay year over year — so the person who learned your system in week one is typically still the one answering your questions months later.

Onboarding starts with the workshop itself. You walk us through your existing architecture and data, we ask the questions that determine what's feasible, and you get a written roadmap out of it. The build phase follows that same architecture decision document, with no separate re-scoping step.

We don't promise a fixed date and hope it holds. We commit to a defined scope from the workshop, a not-to-exceed cost ceiling, and milestone visibility throughout the build — so you always know where the engagement stands.

No. The workshop is a standalone, fixed-cost engagement with no obligation to continue. If you move into the build phase, that also runs time-and-materials with a not-to-exceed ceiling — there's no multi-year commitment attached to either phase.

You can, and a weekend prototype will prove the concept works. What it won't show you is what breaks it in production — data access boundaries, response grounding, cost per query at real usage, and what happens when the model gets something wrong in front of a customer. That's the part we're built to handle; the API call itself was never the hard part.

It gets built into the decision loop itself, not layered on top as a separate screen. The same integration pattern that put MyRealtyWorks' AI directly inside their existing CRM workflow — not a standalone tool alongside it — is what we'd apply to routing, stocking, or supplier decisions: wired into the process your team already runs, not a report someone has to remember to check.

That question gets answered in the workshop, before any build starts — not promised in the abstract afterward. Our default architecture recommendation for sensitive-data integrations is Azure OpenAI, where Microsoft's enterprise terms generally exclude training on customer data. The exact guarantee depends on your specific architecture and contract — the workshop confirms it for your compliance stakeholder before rollout.

No — the AI layer reuses your existing authenticated APIs and stays inside each user's existing permission boundary rather than being granted broader access. That's precisely how MyRealtyWorks' AI CRM layer works today: it can only see what the logged-in user could already see.