AI engineering services
Move from promising demo to governed production system.
ESysApps combines product strategy, architecture, software delivery, AI evaluation, and operational governance so the result can survive real users and real constraints.
Engagements
Choose the smallest engagement that can produce credible evidence.
Scope is shaped around an outcome and a release gate—not an open-ended list of AI features.
01
Enterprise AI assistant implementation
Turn a real business workflow into a published assistant with approved knowledge, scoped behavior, browser-ready UI, and release evidence.
- Use-case and risk framing
- Assistant identity and prompt contract
- Web or workflow embedding
- Quality checks and handoff
02
Governed RAG and knowledge publishing
Design and ship a cited knowledge experience with ingestion, retrieval, evaluation, publishing, and operational controls.
- Knowledge-source strategy
- Retrieval and citation contract
- Evaluation and failure cases
- Published web or workflow experience
03
AI data gateway and read-only querying
Help teams ask natural-language questions over structured data while keeping permissions, source visibility, and audit boundaries explicit.
- Data boundary and access model
- CSV/database/API connection plan
- Read-only query workflow
- Evidence and limitation messaging
04
MCP and agentic workflow automation
Automate a bounded workflow with explicit tool permissions, review gates, test evidence, and a safe handoff to operators.
- Workflow and tool contracts
- Human approval points
- Quality and regression tests
- Monitoring and runbook
05
WordPress + Django AI integration
Connect a marketing site, support site, or internal portal to a governed AI runtime without moving AI logic into WordPress.
- Versioned widget contract
- Production and staging deployment
- Navigation and conversion copy
- Browser QA and rollback notes
Delivery method
Discover, prove, govern, then scale.
- 01
Frame the outcome
Define users, value, risk, exclusions, and measurable success before selecting models or tools.
- 02
Build the thinnest useful path
Connect only the knowledge and actions required to prove the workflow with representative test cases.
- 03
Produce evidence
Measure answer quality, task completion, latency, cost, safety behavior, accessibility, and operational failure modes.
- 04
Release with control
Document ownership, monitoring, approval, rollback, and the next bounded expansion.
What you receive
Artifacts your product and engineering teams can keep using.
The work is designed for handoff, review, and continued evolution—not dependence on hidden implementation knowledge.
- Architecture and decision records
- Versioned code and deployment instructions
- Evaluation cases and release evidence
- Security, privacy, and cost controls
- Operator runbooks and rollback steps
- Prioritized next-wave backlog
Bring one real workflow
Start with the problem your team already feels.
Share the current process, systems, data boundaries, users, and constraints. We will identify the safest useful first release.