Specialized AI assistants
Give your team an assistant with a clear job: find answers in approved documents or collect the information needed for a request. We build, test, and review it with you before release.
Plain-language overview below. Technical details available further down.
Approved sources support the task. Configured tools define the available actions. People review the build before release, and activity records help them inspect the result.
What we can build today
Our current agent factory supports document assistants and structured intake assistants. Each engagement starts with one workflow and agreed tests, using synthetic data for the initial demonstration.
Help staff find the right policy, procedure, or handbook answer without searching through folders. The assistant searches approved material, points to its source, and is tested to acknowledge when the information is missing.
Example: Ask what an expense policy allows and see the document behind the answer.
Turn an unstructured request into a useful draft record. An intake assistant follows agreed fields, asks for missing information, and prepares a local record for a person to review.
Example: Gather the details needed to review a service request, with required fields checked before saving.
For suitable workflows, the assistant can use a model running on local hardware. We agree the information it may use, where processing happens, and what is recorded before implementation.
Local runtime and the tools used to build the assistant have separate data flows. Cloud-based building tools may be used by agreement.
Each assistant has a defined role and a version your team can review before release. Behavior checks exercise its intended tasks and limits. Activity records help you inspect requests, tool use, and results.
Your handover includes the agreed scope, acceptance evidence, and instructions to start, stop, and use the assistant.
The current foundation uses approved local text documents and local draft records. Other file formats, external applications, and client deployment requirements are assessed and scoped separately.
Demonstrated internally · synthetic data
In our accepted internal walkthrough, a factory-built handbook assistant found the hotel allowance in a fictional staff policy, cited the document, and declined a request to book a room. The same question was checked again after the application and assistant restarted.
This demonstrates the tested workflow. It is an internal example, not a client case study or a promise that every future answer will be correct.
From a task to a working assistant
Our internal agent factory supports the engineering process. You work with Vectis to define the outcome, review the result, and decide whether it is ready for your use.
Choose one task, a responsible owner, approved sources, allowed actions, and examples of a good answer or record.
Our agent factory turns the agreed specification into an assistant, using a selected coding tool or local model.
Check the code and behavior, including missing information and requests outside its role. Failed checks lead to a limited repair cycle or investigation.
A person reviews the candidate and its evidence. The release process delivers the exact version approved, with its history recorded.
Start the assistant, try your agreed tasks, and confirm it works after a restart. Ongoing care can cover source updates and scoped improvements.
To start, bring one task, a small set of approved sample documents or required fields, a few example requests, and the actions the assistant must not take. We agree scope, data handling, acceptance criteria, timing, and fees before implementation.
For your technical team
The same capabilities, with more detail about the tested foundation and the requirements we assess for each deployment.
Supported foundation. Document question answering and structured intake. Tools search and read approved local text or Markdown sources, and save local draft records. PDF extraction, spreadsheets, external connectors, and additional agent types require separate implementation and qualification.
Build and release. The internal pipeline supports cloud coding tools and a local-model builder. Candidates undergo code and behavior checks, bounded repairs, and version-specific human release approval. Build, review, delivery, and restart were exercised in the accepted local release.
Runtime and data. Qualified assistants run on a local model. Runtime processing, build-time access, provider use, retention, and logging are separate deployment decisions. Local execution alone does not establish that all project information stays on one machine.
Access and isolation. The tested foundation uses configured source folders and application-level tool restrictions. These are not an operating-system sandbox or a complete enterprise access-control system. Builders run with the operator's permissions; the local Studio has no separate approval login. Authentication, isolation, hosting, and client security requirements must be addressed for the intended environment.
Evidence and operations. Version history, evaluation outcomes, approvals, and activity records support investigation and review. Results from representative synthetic cases do not establish accuracy for every workload. Availability, monitoring, response expectations, and ongoing support are agreed in the deployment scope.
Further review. Detailed specifications, data-flow diagrams, evaluation scope, and deployment requirements can be provided during scoping. Proprietary build instructions and internal operating details are not published here.
A practical starting point
If a conventional integration or a simpler automation fits better, we will scope that approach. The goal is a useful result your team can understand and operate.
Identify approved sources, gaps, and ownership before building.
Agent-Ready DataDefine who owns the workflow, who reviews it, and which decisions stay with people.
AI GovernanceExplore our other services: Vectis Front Desk, eFile & integrations, and Workflow Sprints and Ongoing Care.
One task. A clear first step.
Tell us what your team needs to find, collect, or review. We will help define a first assistant and the evidence you need to evaluate it.
