Anvayric / Applied AI engineering
Agents, data integration and intelligent workflows. Engineered around your information, your software and the people using it.
Agents. Data integration. Intelligent workflows.
Three connected engineering areas. Each engagement defines the application, its data access and how the work should behave. New agent and MCP capabilities begin with feasibility.
Scope assistants and agents that interpret information and use permitted tools. Establish when a fixed workflow is sufficient, what needs approval and how results will be evaluated.
New agent work begins with feasibility. IMFIT does not establish a deployed autonomous agent.
Data integrationBring records, documents and existing software into an AI application through APIs, retrieval or MCP where appropriate. Define access, source ownership and update behavior.
IMFIT evidence: approved knowledge, versions and source references. MCP delivery is not demonstrated.
Intelligent workflowsCombine model output, business rules, application state and human review. Agree how interrupted work, failures and handovers should behave.
IMFIT evidence: stale-response checks and staff-takeover controls. Each new workflow needs its own tests.
IMFIT / Implementation walkthrough
IMFIT is our own operating business. Its software connects enquiries, business knowledge and staff controls. Here are the mechanisms you can inspect—not a promise that every business needs the same system.
Illustrative enquiry
“What are your opening hours?”The question is simple. The information and conversation can change while the answer is being prepared.
Knowledge is filtered by approval status and effective dates. Source identifiers and versions remain available in the response context.
Inspected source + selected local testsA transaction compares the expected inbound message with the latest one. An older result is rejected when they no longer match.
Inspected source; demo reconstructs the checkThe outbound gate checks human takeover and persistent opt-out. Assigning a staff member alone does not mean takeover.
Inspected source + selected local testsChange a knowledge version, prepare a reply and test an interruption. The public demo uses fictional records and deterministic responses; it does not connect to IMFIT production or a live model.
Evidence scope: inspected source and selected local tests. See the case study for test coverage and the distinction between implemented controls and illustrative demo behavior.
Why commission Anvayric
A direct engineering relationship for teams that need a custom AI capability, with scope and availability agreed before work starts.
IMFIT is our own business. Its changing knowledge, conversation state and staff controls inform how we approach application design. You can inspect those decisions in the case study.
Review the evidenceWork directly with the founder on discovery, implementation reviews and handover. Agree which data to connect, what AI should do and where rules or people should remain in control.
Meet the founderThe proposal names the deliverables, acceptance cases, code and licensing terms, operating costs and support boundary. Discovery can recommend an existing tool when custom work is unnecessary.
See the engagement processBring an example of the work, the systems involved and the person responsible for the result. We first discuss fit, then agree the scope and fee for discovery.
Research / Not yet demonstrated
When does a workflow need an agent? When is a smaller model sufficient? Which tool connections make review easier?
These are proposed investigations. We have not established a production autonomous agent, delivered MCP implementation or custom-trained small model through the IMFIT case.
Read the research agendaRead the IMFIT implementation noteScoped AI engineering
Start with the work, the information it needs and the question you want to answer.