Data integration
Connect your information.
Preserve its meaning.
Make selected records, documents and software capabilities available to an AI application. Define which source to trust, who can access it and how changes reach the system.
Design pattern / Proposed project scope
The system
around the capability.
Discovery turns this pattern into a specification for your systems, users and operating constraints. It is not a claim of a completed deployment.
- Source
Records, documents and existing software
- Context
Ownership, identity, permissions and versions
- Interface
API, retrieval or MCP where appropriate
- Maintain
Updates, failures and recovery
Illustrative commission
A shared knowledge layer across selected systems.
A team works across documents and operational records. Scope a connection that retains source identifiers and access rules, so an application can retrieve relevant information and reveal stale or unavailable sources.
- Source map
- Authoritative sources, record identifiers, update owners and access boundaries.
- Connection
- An agreed API, retrieval or synchronisation path. MCP is considered when compatible AI clients need a tool interface.
- Evaluation
- Changed and missing records, revoked access, unavailable systems and failed updates.
- Handover
- Connector code, configuration, dependency costs, source-maintenance and recovery instructions.
Example scope to assess and tailor. Deliverables, fees, dependencies and acceptance criteria are agreed before implementation.
What supports this work
Experience to inspect.
New work to evaluate.
IMFIT demonstrates approved knowledge, versions and source references. New external connectors, permission models and MCP servers require their own implementation and tests; no delivered MCP deployment is claimed.
Inspect the IMFIT case studySee how we scope and deliverScoped AI engineering
Have a task in mind?
Let’s assess the fit.
Start with the work, the information it needs and the question you want to answer.