Engineering proof before the sales call.

Review the standards behind an IdeaBosque build before a call: consistent Model Context Protocol (MCP) modules, typed connectors, tests, rate limits, audit logs, deployment instructions, and operator runbooks. The deliverable is production code your team can own, or a managed platform we operate with you.

Request a scoped buildSee the RFQ architecture
01—MCP Module Standard

One module pattern, every connector.

MCP_CONFIGURATION

Every module exposes its capabilities through a uniform MCP_CONFIGURATION declaration: tools, resources, and prompts. The orchestration backbone and review process know exactly what to expect.

  • Tools, resources, and prompts declared in MCP_CONFIGURATION
  • Shared auth, error, and rate-limit patterns across all modules
  • Python 3.8+ compatibility - modules run on legacy infra without runtime upgrades
  • Uniform testing expectations: every tool has request/response tests and error-path coverage
02—Connector Delivery Pattern

Every connector ships with the same operational posture.

Typed client + governance

  • Typed client with explicit method signatures and return types
  • Auth handling: OAuth, API keys, and mTLS managed per connector, never hardcoded
  • httpx HTTP/2 with exponential backoff and jitter
  • Pluggable rate limiters (token bucket, sliding window, per-system)
  • Structured errors with HTTP status, provider error code, and retry classification
  • Audit logging: every request and response logged with latency and outcome
  • Test coverage: integration tests against sandbox, mock tests for error paths
03—Agent Runtime Pattern

The orchestration backbone is the deliverable.

AI agent orchestration backbone + operational posture

  • The orchestration backbone coordinates the agent, routes tool dispatch across MCP modules, and enforces rate limits
  • System prompt and tool descriptions version-controlled alongside module code
  • Session store for multi-turn conversations with configurable retention
  • Audit logs for every tool call: request, response, latency, outcome
  • Observability: structured logs, metrics, and traces shipped to your monitoring stack
  • Fallback paths: graceful degradation when a backend is unavailable
  • Operator runbook: deployment, rollback, incident response, and common operations
04—Data & Knowledge Graph Pattern

The graph complements the system of record; it never replaces it.

Neo4j + Dagster

Neo4j stores the static catalog graph: product relationships, supplier mappings, substitute rules, and margin configurations. Live operational state such as inventory levels, order status, and availability stays in the source systems where it belongs.

  • Neo4j for relationship reasoning: alternative suppliers, substitute parts, category hierarchies
  • Dagster for ELT: assets, ops, schedules, and sensors declared, versioned, and observable
  • Hive-partitioned files in S3, watermark-based incremental loads, idempotent writes
  • dbt for transformations into Redshift / Athena
  • Live state stays in the system of record; the graph does not become a second source of truth
05—Public Artifacts

Engineering writing and sample plans you can request before a build.

06—Engagement Shape

Fixed-scope phases, weekly demos, and a defined handoff.

How engagements are scoped

Discovery is one week and produces the system inventory, workflow map, and build plan. Typical first builds run 5-8 weeks total. You know the full scope and cost after Discovery before committing to the build, and every phase ends with working software or a concrete handoff artifact.

Send your source systems, workflow boundaries, and target handoff.

The more specific you are about backends, constraints, and handoff, the faster an engineer can confirm fit, risk, and the smallest useful first build.

Send project brief