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CapabilitiesEnterprise AI Applications

AI Access Layer for Existing Platforms

Your platform already holds the data, rules, and operations your teams need. Provectus builds a layer that lets AI tools work with them under each person’s permissions.

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The shift

Teams work in AI tools that cannot reach the platform’s data

Teams already think and write in Claude, ChatGPT, and other AI tools. The platform’s data reaches them only by export and copy-paste. Anything more needs a ticket.

  1. 01

    Questions the screens cannot answer

    The answer spans several modules, so someone assembles it by hand.

    “What explains the $500,000 profit gap? Check the records and what the teams reported.”

  2. 02

    Workflows nobody built

    A better way of working waits in the backlog as a development task.

    “Could we move stock from another warehouse and rush only part of the order?”

  3. 03

    Each role needs its own view

    Each team wants its own view of the same data. No platform anticipates them all.

    “Who can approve this payment, and can it still be processed today?”

Provectus brings the platform into those AI tools

A custom layer over the existing platform, with minimal changes to the platform itself.

  • New uses for your data

    Ask across modules and build the analysis nobody planned a screen for, from the platform’s own records.

  • Agentic automation

    Agents take on routine work across the platform: they gather the facts, prepare reports and updates, and draft changes for review.

  • Signals

    Agents watch for a profit gap, a stock risk, or a stalled payment, and flag it to the right person with the records.

  • Your access model, respected

    Every request runs with the permissions of the person asking. The agent sees only what that person can see.

See the demo

The demo

Work across systems in one conversation

The chat interface you already know now connects to your company’s data, tools, and business knowledge. Follow four example conversations from the first question to a result ready for review.

Quarterly financial review Saved
1/1

Continue this session

Illustrative session · synthetic data · no live connections

Styled after Claude Cowork. Follow-up actions change only this demo.

One-pager

Q3 financial review

How we work

The first answers come early, and new uses follow over time

  1. 01 · Understand the platform

    We map the data model, rules, and permissions, and agree where to start.

  2. 02 · Build the layer

    We build the context, integrations, and tools, and test them on real tasks.

  3. 03 · Put it to work

    We connect your AI tools, train people, and refine the setup as needs grow.

What goes into the layer

AI tools like Claude bring the reasoning. The layer brings what they cannot know: your data, your rules, and who may see what.

Used from Claude Claude Code ChatGPT Custom agents
Platform context

What the data means

One shared description of your platform. Every tool works from it, so they all use the same terms.

  • The data model
  • How records relate
  • Business terms
  • Rules and calculations
So every tool knows

“Operating profit is revenue minus direct costs and operating expenses. It excludes financing costs and income tax.”

  • Skills

    How your team does recurring work

    Reusable instructions an agent loads for a recurring task, with your team’s methods and the output you expect.

    So the agent knows

    “A quarterly report uses the posted ledger, lists unposted items separately, and waits for the controller’s sign-off.”

  • Agents and workflows

    Work people can delegate

    Platform tools combined into tasks people can hand off, with access and review rules defined for each workflow.

    So the agent knows

    “The planner may submit transfer requests. The regional lead approves extra freight up to $750.”

  • MCP integrations

    The connection, as the person asking

    MCP is the open standard AI tools use to connect to business systems. The integration opens the platform’s data and operations with the permissions of the person asking.

    So the agent knows

    “This connection permits work profiles and availability, not individual compensation.”

Your platform

Data, operations, and permissions

The data model, business logic, and screens stay as they are. Provectus adds a few endpoints and a sign-in path.

Stays in charge

The platform checks every call against the permissions of the person asking. Switch the layer off and it runs as before.

The process

How a platform's capabilities become tools an agent can call

The agent turns a plain-language request into calls the platform understands, such as SQL queries and API requests. MCP is how those calls reach the platform.

One request, traced

You

You tell the agent what you want, in plain words.

A chart, a table, a written brief, or just the answer.

The agent

reads the ask · plans the steps · picks the tools · writes the sql

Builds what you asked for.

The MCP layer

list_tools

The first call: the agent asks what this layer can do. These four tools come back.

get_docs

Your data model in plain text. The agent reads it before touching data.

requests your platform's data model docs
get_current_user

Reads who is asking and what their role allows.

requests your platform's api
query_data

The agent writes SQL from those docs. One tool answers any question, under row limits.

requests your platform's database api custom service
update_…

Writes go out as a preview first. Nothing applies without a yes.

requests your platform's api

Platform

data model docs

written during the build

database

queries run off a read replica

api

roles, access rules, and the same write path the UI uses

custom service

your own services: money, inventory, domain jobs

Every request starts as a sentence and comes back with records. The layer in the middle is the only new part: it exposes a chosen set of the platform's capabilities as tools and talks to the platform as the person asking.

The business case

Your teams can complete more work

AI access expands what each person can do with the platform. The value comes from shorter process times and less dependence on help from other teams.

Measure the first workflow before expanding

Choose a recurring task and compare time to a reviewed result, manual handoffs, and corrections before and after activation. Weigh the improvement against implementation and ongoing operating costs before expanding.

  1. 01

    Decisions based on data speed up processes

    Agents bring current records and relevant context together. Less time spent collecting and reconciling information means teams can reach informed decisions sooner and keep work moving.

  2. 02

    Work needs fewer handoffs

    With an agent’s help, one person can complete a task across connected systems. Fewer transfers between people and teams mean less waiting and less lost context.

  3. 03

    People can handle a wider range of work

    AI helps people use the platform’s data, tools, and reusable methods beyond their usual specialty. Each person can take on more kinds of tasks independently, with specialist support where needed.

  4. 04

    Routine work creates fewer bottlenecks

    Agents can handle repeated requests for information and routine checks. Fewer tasks wait on the same specialists, so their availability is less likely to hold up the process.

The questions

Before you get started

Is this an off-the-shelf product?

No. Provectus builds the layer on the platform you already run: its data model, rules, and permissions. The demo shows the shape of the result. Your layer carries your platform’s terms and your team’s methods.

Will you change or rewrite our platform?

No full rework. The data model, business logic, and screens stay as they are. Where the platform has no way to expose the data or an operation a workflow needs, we add it: an API, a module, a sign-in path. The first step, understanding the platform, shows how much of that your platform needs.

Do we need all of it: MCP integrations, skills, and agents?

Not all of it, and not at once. The first scope is usually one read-only MCP integration and the skills the first workflow needs. Agents and automation come when a task is ready to delegate. We pick the set from your situation, not from a catalog.

Our AI use is limited to an approved environment such as Amazon Bedrock. Does this still work?

Yes, with one difference. Where your organization allows Claude, Claude Code, or ChatGPT, those tools connect to the layer directly. Where only Bedrock is approved, the agent runs in your AWS account, with private connections to the model service and your platform’s MCP tools, and we design that deployment around your network and access rules. A fully offline environment needs a separate design with locally hosted models.

How do we control what agents can see and change?

In most cases the layer uses the platform’s role model as it is: a request runs with the access the person already has, and existing access levels stay in place. Write access is a separate decision for each workflow, and proposed changes go to a person for review. Where an AI tool can approve its own actions, that connection stays read-only. The exact setup depends on the task, and we agree on it with your team before enabling changes.

Which workflow should we start with?

Start with a recurring process slowed by information gathering, handoffs, or dependence on a few specialists. Together, we define a useful result and how to measure it. The first scope uses read-only access so we can test the answers against real work before adding actions.

What do we need to start?

An onboarding with the people who know the business process and those who own the platform, and approved access to the platform for the first workflow. Your team explains the rules and reviews results. Provectus builds and tests the layer, connects your AI tools, and helps people learn to use them.

What determines the timeline and cost?

The main factors are the first workflow’s scope, the systems it needs, and how much work is required to document the platform and enforce its access rules. We assess these with your team before proposing the build. The business case should also account for ongoing AI usage, maintenance, and support.

Tell us about your platform and the work you want AI to help with
We will review how the platform works today and propose a first scope, with a way to measure the result.
Start the conversation →