Discuss your projectYour team gets answers from approved records with the source shown, replies drafted for a person to approve, and routine updates made within limits you set. It works inside the tools they already use, with every action audited.
Support, sales, operations, and HR leaders whose teams look things up in several systems, type the same kinds of replies, and update records by hand. The assistant sits in the tool they use; it does not become another tool.
The workflow
The steps we see most often in this kind of operation.
Features
Long conversation histories condensed into a short summary with the customer’s issue, what has been tried, and the next step, ready for handover.
Fields pulled from invoices, applications, contracts, and emails into structured records, with low-confidence values flagged for a person to check.
Incoming requests tagged by topic, urgency, and customer, then sent to the right queue or person according to rules your team controls.
Step-by-step prompts that walk newer team members through policies and procedures in context, drawing on the same approved knowledge sources.
Agents rate answers and correct drafts inside the tool, and those signals feed the evaluation so quality problems surface before they spread.
Views showing where the assistant declined for lack of a source, so content owners know which articles or policies to write next.
Your first release
How the work runs
We start with the people doing the work and the systems around it, then write down the first release: what it includes, what it leaves for later, the integration decisions, and the acceptance criteria. Nothing is built until that scope is agreed.
We build in releases you can review as the work takes shape, using realistic data. Every release carries tests, a security review, and documentation. Integrations are tested against real records, with exceptions visible.
Your team receives the documentation, the runbooks, and the training to run the system, and we stay on for continued engineering when you want it.
Scope and price
Every project is priced from a written scope. These are the factors that move it most.
Current, well-organized records and policies can be indexed directly, while scattered or conflicting documents need cleanup and ownership decisions before answers can be trusted.
Carrying document and record permissions through to the assistant takes design and testing, particularly where teams, regions, or customers see different information.
Answers only, drafts for approval, or bounded actions in other systems each add integration work, limits, audit requirements, and evaluation effort.
Testing on a large, representative set of your own cases, including edge cases and sensitive topics, adds effort but gives a reliable picture before rollout.
Services company
On a services company’s support team, a typical question means opening the customer record, the job, the invoice, and the policy document before typing a reply. An assistant inside the ticketing system can read those approved sources, draft the answer with each source shown, and leave the agent to edit and send it. When nothing matches, it says so instead of guessing. Before rollout, it is evaluated on a sample of the company’s past tickets. A later stage can let it create follow-up tasks within a written limit, with every action in the audit trail.
Questions
What buyers ask before a project like this starts.
It answers only from approved sources and shows which one. When nothing matches, it declines. That behavior is tested before rollout on your own cases.
For routine actions within written limits, yes, with an audit trail. Anything over the threshold is proposed and waits for a person.
We agree on it in writing before anything is built: what a model may see, where it is processed, and what stays inside your systems.
It lives in the tool the team already uses: the ticketing system, the CRM, or the chat tool, rather than a separate window.
The model is chosen for the task, the data sensitivity, and the hosting requirements, and it can be changed later because the assistant is built around a model layer rather than a single provider. We compare candidates on your own cases before choosing and repeat that evaluation whenever a switch is considered.
Sources are re-indexed automatically when documents or records change, and retired documents drop out of the index. Content owners can see which sources the assistant relied on most, and a regular evaluation run confirms that updated policies are reflected in the answers people receive.
Trust comes from seeing the source behind every answer, approving drafts before anything is sent, and knowing the assistant declines when it lacks evidence. We roll out with a small group first, collect their corrections, and widen access once the evaluation and their feedback show it is helping.