Agentic AI Systems
PydanticAI, MCP, RAG/GraphRAG - End-to-end AI agents
Overview
We deliver end-to-end question-answering and decision support systems over multi-domain data. Our expertise lies in designing PydanticAI agent graphs with typed states/IO, tool-calling, and model fallback (OpenAI + Anthropic) under explicit token/latency budgets.
We standardize data access via an MCP tools layer (e.g., sales, advertising, content, keywords) with versioned schemas, validation, and safe parallelism. The APIs behind the agents are LLM-optimized FastAPI + MongoDB services with projection-level filtering, pagination, and vector/graph retrieval for RAG/GraphRAG.
Reliability is built-in: client-side rate limits, Tenacity backoff with jitter, token accounting, request-scoped tracing, and LLM run instrumentation (e.g., Langfuse/Logfire). Delivery is cloud-native (AKS/GKE) with CI/CD (CircleCI/GitHub Actions) and blue-green/canary strategies, so answers arrive quickly, safely, and with auditable evidence.
Technical Excellence
Approach & Methodology
- Planning: Agents decompose questions → select tools → define metrics/time windows
- Tooling: MCP exposes domain tools (sales, advertising, content, keywords) with typed inputs
- Retrieval: RAG/GraphRAG to inject facts and relationships; context windows budgeted with truncation rules
- Synthesis: Structured evidence objects (numbers, time series, top drivers) rendered into concise narratives
Technology Stack
- AI/Agents: PydanticAI, OpenAI/Anthropic
- Backend: Python, FastAPI, Pydantic, AsyncIO, httpx
- Data: MongoDB (motor, pooling, indexing, projection filtering)
- Infrastructure: Docker, managed Kubernetes (HPA, probes), CI/CD
- Reliability: Rate limits, retries with backoff, token budgets
Measurable Impact
Expected Results
- Faster time-to-insight with trustable outputs and audit-ready evidence
- Lower latency/cost via precise payloads and bounded parallelism
- A stable tools layer (MCP) that accelerates adding new capabilities
KPIs We Track
- Time-to-first-answer
- End-to-end p95 latency
- Answer quality acceptance (stakeholder sign-off rate)
- Cost per 100 queries (LLM + infra)
- Cache hit rates
How We Work Together
Discovery → Build/Integrate → Harden → Operate
Weekly demos ensure alignment and rapid iteration. Each phase has clear deliverables:
- Discovery (1-2 weeks): Use cases, ROI, target architecture, data contracts
- Build & Integrate (4-8 weeks): Agents, MCP tools, services, RAG, observability
- Production Hardening (2-3 weeks): Autoscaling, probes, CI/CD, SLOs/runbooks
- Managed Support (ongoing): Reliability, cost tuning, knowledge/tool expansion
Ready to Get Started?
Let's discuss how Agentic AI Systems can transform your business