We build AI agents that answer the same fifteen questions your senior people answer every week. Trained on your actual policies, your actual documents, your actual edge cases. With a "I don't know, here's who does" answer wired in for everything else.
01 / What's at stake
There's someone in your company who knows the answers. Your most experienced support rep. Your sales engineer who's seen every objection. Your ops manager who knows where the return policy bends and where it doesn't. Their hours are the most expensive hours you pay for. And they spend a third of them answering the same questions they answered last Tuesday.
You've thought about a chatbot. You've also seen what bad chatbots do. Confidently wrong answers, customers escalating angrier than they would have without one, your senior person now cleaning up the bot's mess on top of doing their actual job. So the questions keep coming, the senior person keeps answering them, and the work that needs a senior person keeps waiting.
There is a better version of this. The agent answers roughly seventy percent (the typical pattern across our agent builds, varies by domain clarity) of repetitive volume that has clean answers in your documents. The other thirty percent (the edge cases, the judgment calls, the "this is unusual" cases) go straight to the named human who should see them. The senior person stops being a search engine. They go back to being a senior person.
Hallucination is a scope problem, not a model problem. Build the scope right and the agent stops lying.
02 / What we build
Every agent we ship does one job, knows where its job ends, and hands off the rest to a human you've named.
01 · Tier 1 support
Trained on your help docs, policy pages, and past support transcripts. Handles the seventy percent of inbound that has a clean answer. Routes the rest to your support inbox with full conversation context attached so your team doesn't restart from zero. Every customer-facing build ships with a clear escalation path; we won't put an agent in front of a paying customer without one.
02 · Team knowledge
Sits in Slack, Teams, or a private web app. Trained on your policy docs, SOPs, onboarding material, and whatever else lives in three Notions and a shared drive. New hires ask it the questions they're embarrassed to ask in #general. Tenured staff ask it the policy questions they keep forgetting. Comes with monthly retrieval health checks for the first quarter.
03 · Read, extract, decide
RFP responses, vendor contracts, incoming invoices, intake forms. Anywhere a person currently reads a document and types fields into a system. The agent reads, extracts to your schema, and either writes the result back or flags the lines that need a human eye. Built with eval suites so you know which document types it handles cleanly and which it punts on.
04 · Hands and feet
Hooked into the systems the agent needs to actually do its job (your CRM, your help desk, your scheduling tool, your inventory database). Ticket lookup, account context, order status, calendar availability. The agent stops being a Q&A box and starts being a teammate that can check things and do things. Built on tool use, scoped to read-only by default, write-permissions added explicitly per action.
05 · The handoff
Every agent we build has a documented out-of-scope behavior. Out-of-scope questions get a short honest answer ("I don't have that information") plus a hand-off. A Slack ping to the on-call person, a ticket in your help desk with the conversation attached, an email to the named owner. Escalation rules are configured per question type so the legal questions go to legal and the billing questions go to billing.
03 / How we work
01 · Scoping
Thirty minutes. We walk through the question or document workload you want the agent to handle, look at your source documentation, and decide whether the docs are ready for retrieval or need a cleanup pass first. If the answer is "not yet," we say so and tell you what to fix.
Free · 30 minutes · Honest answer either way02 · Source-of-truth check
One week. We audit the source documents the agent will retrieve against. If they're clean, we move on. If they have stale info, contradictions, or gaps, we scope a cleanup pass (sometimes ours, sometimes yours). The principle: we don't ship an agent that retrieves from a stale wiki, because the agent will retrieve confidently and the answer will be confidently wrong.
Week 1 · Fixed-fee scope · Cleanup separately priced if needed03 · The agent itself
Two to four weeks. Vector store wired to your documents. Retrieval tuned against a question bank we build with your team. Scope boundary explicit. Every question type either has a confident answer or a routed handoff. Eval suite runs nightly so you can watch the answer quality improve as the system tunes.
Weekly demos · Live eval results · You watch it learn
04 · Launch
Production deployment to Slack, web widget, or wherever the agent lives. One-hour training for the team owner on how to read retrieval logs, update source documents, and tune the scope. Documented runbook covering what to do when the agent says something wrong, how to add new question types, and how to roll back to a safe version.
Week of launch · Documented runbook · One-hour training included
05 · First-quarter backstop
Monthly retrieval health check for the first quarter. We look at which questions the agent answered well, which it punted on, which sources got stale, and whether the scope boundary held. Adjustments included in this window. After ninety days you either take it in-house or move to a maintenance scope.
90-day backstop · Monthly review · Optional ongoing retainer04 / Proof
A voice-trained agent pipeline that turned six hours of weekly executive content work into a five-minute review. Twenty posts a month, in her voice, on schedule.
An agent fleet running Google Business Profiles across 80+ client accounts. Three hours daily of manual posting became two hours weekly of oversight. 1,200 posts a month, none from a template.
05 / Questions
A custom AI agent is an internal tool or single-purpose assistant. One job, scoped tight, $3K to $15K range, shipped in weeks. AI Product Development is an end-user product with multiple roles, dashboards, transactional flows, and a revenue mechanic (think Show Me The Bids, the two-sided roofing marketplace we built in six weeks). If your customers will log in and pay to use the thing, it's a product. If your team uses it to answer questions or process documents faster, it's an agent. When in doubt, the discovery call sorts it out.
Three things. First, retrieval against your documents. The agent answers from your wiki, your policies, your help docs, not from the model's training data. Second, an explicit scope boundary. For everything outside the agent's domain, the answer is a short honest "I don't have that" plus a handoff to a named human. Third, an eval suite we build before launch so we know which question types the agent answers cleanly and which it punts on. Hallucination is a scope problem, not a model problem.
Sometimes. Sometimes not yet. The week-one document audit tells us which. If your sources are clean enough to retrieve against, we build. If they have stale info, contradictions, or gaps, we scope a cleanup pass before the agent gets near them (usually one extra week). We won't ship an agent retrieving from a stale wiki because the agent will retrieve confidently and the answer will be wrong with conviction. Better to spend a week cleaning the source than six months apologizing for the output.
Usually not, and that's not the goal. The pattern that works: the agent handles the bulk of repetitive volume that has clean answers in your documents, and your senior person handles the rest that needs judgment. Headcount doesn't drop. The backlog does. The senior person gets to do the work that pays their salary instead of the work that wastes it.
Two to six weeks depending on scope and document state. Cost depends on the agent's job, the size of the document corpus, and the integrations it needs. We don't list prices on the page because the range is honest. A Slack-based knowledge agent on clean docs is a different scope from a document-processing agent wired into your CRM. The discovery call ends with a proposal within forty-eight hours, anchored to a fixed scope and a fixed price.
Source documents are yours to update. The agent retrieves against whatever the current version says. Most clients update their wiki or policy docs the same way they did before the agent existed; the agent simply reflects the current state. For agents on documents that change often, we wire automated re-indexing into the build so policy updates propagate within an hour.
Next step
Five minutes for the free assessment if you're sketching the idea. Thirty minutes with us if you already know the agent you want and want to scope the build. Proposal within forty-eight hours either way.
