Services

AI architecture services for serious technical teams.

Focused advisory and implementation support for founders, CTOs, AI platform teams, and infrastructure companies moving from experiments to production systems.

Architect's Systems Sandbox

Simulate cost, latency, and system execution.

Use the interactive tools to route your advisory requirements, run cost & latency simulations, or trigger live-simulated terminal traces of Manoj's production patterns.

architect_sandbox_hub.exe
// ARCHITECTURE ROUTING OUTPUT

LangGraph Consultant Engagement

Based on your selections, Manoj will customize an engagement to resolve 4 - 6 weeks scope bottlenecks, building structured templates and running code optimizations.

Expected System Deliverables:
  • Explicit LangGraph state machine flow mapping
  • Deterministic tool routing and failover checkpoints
  • Multi-agent regression evaluation datasets
  • Human-in-the-loop validation gate integrations
Scope Summary Config:
AI ARCHITECTURE ENGAGEMENT SCOPE SUMMARY
------------------------------------------
Primary Focus: LangGraph Consultant Engagement
Bottleneck: Agent Loops are opaque, brittle, or fail in production
Scale Target: Scaling to Production (10k+ runs) (4 - 6 weeks scope)
Infrastructure Stack: Kubernetes / RedHat OpenShift / NVIDIA Run:AI

Expected Key Deliverables:
- Explicit LangGraph state machine flow mapping
- Deterministic tool routing and failover checkpoints
- Multi-agent regression evaluation datasets
- Human-in-the-loop validation gate integrations
Conversion Hub

Submit below to automatically compile this specification and load your local mail composer to email Manoj. Or connect via LinkedIn.

"Hi Manoj, I used your AI Architecture Simulator and was impressed by the telemetry console. I'd love to connect and discuss optimization options for our production LLM pipelines."
Submit & Open Email Spec
Connect on LinkedIn Book 15m Call
Telemetry: OK // Trace connection validatedLinkedIn Email Book Meet

Offer Map

Choose the problem surface.

Each engagement is designed around a concrete architecture constraint: agent reliability, retrieval quality, backend infrastructure, platform deployment, or technical adoption.

AI Architecture Advisory

I provide senior architecture guidance for teams moving AI products from vague ambition to a production-ready operating model.

  • Clarify system boundaries, platform choices, and delivery risk
  • Define POC-to-production roadmap for AI-native products
  • Review reliability, governance, evaluation, and deployment strategy

LangGraph Consultant

I help you design agentic workflows with explicit state, deterministic routing, short/long-term memory, robust fallbacks, and human-in-the-loop validation gates.

  • Model agent workflows as debuggable state machines
  • Reduce brittle tool-calling and hidden orchestration behavior
  • Build evaluation paths for multi-agent reliability

RAG Infrastructure Consulting

I optimize retrieval quality, precision grounding, querying latency, and user trust for knowledge-heavy, vector-driven AI systems.

  • Design hybrid retrieval and pgvector indexing strategy
  • Improve chunking, ranking, grounding, and answer quality
  • Create evaluation datasets and regression loops for RAG

AI Platform Engineering

I build maintainable backend systems for AI platforms, leveraging async FastAPI workflows, structured schemas, queue-based workers, and robust cloud/container topologies.

  • Ship FastAPI AI services with production contracts
  • Design queues, traces, workers, model gateways, and cost controls
  • Deploy workloads across cloud, container, and enterprise environments

DevRel Engineering Partnerships

I turn complex AI infrastructure products into credible reference architectures, production-grade templates, and developer adoption assets.

  • Build reference architectures and demos that developers trust
  • Create technical writing that speaks to senior engineers
  • Translate infra value into implementation-ready education

Fractional AI Architect

I join your team as a fractional architect to bring senior solutions judgment, review infrastructure plans, and mentor builders before a full-time hire is needed.

  • Review architecture and unblock technical decisions
  • Mentor engineers on AI-native delivery patterns
  • Create delivery cadence for production AI initiatives

Delivery Pipeline

Discovery to scale: how I implement.

I do not operate as an ad-hoc freelancer. I lead engagements through a strict, transparent system pipeline to guarantee that your production workloads are maintainable and debuggable.

01

Discovery & Failure Audit

Audit raw notebooks, prototype API graphs, or ingestion layouts. Identify execution bottlenecks, hallucinations risk, and token costs.

02

Technical Contracts Specification

Decompose query intents and map state variable schemas. Establish execution queues, API data structures, and latency constraints.

03

Stateful Graph & Infrastructure Ingestion

Ingest vector indices, write explicit LangGraph transitions, build model gate routing, and deploy telemetry collectors.

04

Regression Evaluations Testing

Run automated tests against faithfulness metrics, chunk recall ratios, and latency. Iterate steps to guarantee system safety.

05

Platform Hand-off & Observability Handoff

Deliver production repositories, Docker/Kubernetes configurations, and host system run-through alignment sessions.

Work With Me

Bring an AI system worth architecting.

Bring the hard system constraint: retrieval quality, agent failure modes, latency, evaluation, deployment topology, or technical market education.

Start Advisory IntakeView GitHub