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Ashok

Independent consultant · Noida, India · remote worldwide

I put AI into production.

Nineteen years of architecture and engineering leadership, most of it on systems used by millions. I consult on AI strategy, retrieval and knowledge graphs.

Ashok VishwakarmaEst. 2007
Ashok Vishwakarma

Consulted for

01 / What I do

Most AI work I am called into is not a model problem. It is an architecture problem wearing a model's clothes.

01 /

AI strategy

Which problems justify model spend, which are ordinary engineering, and what the sequence is once the board stops asking for a chatbot. Written down, costed, and owned by you.

02 /

Retrieval and RAG

Ingestion, ranking, evaluation and a latency budget designed together rather than bolted on. Retrieval that holds its accuracy as the corpus grows and the questions get harder.

03 /

Knowledge graphs

Neo4j schema, ontology and traversal written for the domain, going past nearest-neighbour matching so answers carry structure and provenance.

AI strategy and roadmap audit2 to 3 weeks, fixed fee
RAG system design and build8 to 12 weeks, milestones
Knowledge graph modelling in Neo4jProject or embedded
Architecture review and technical due diligence1 to 2 weeks
Fractional CTOMonthly, named days
Team enablement and trainingWorkshops, in situ
Performance and scale engineeringProject

Fees are quoted per engagement after the first call. I take on two clients at a time.

02 / At scale

Nineteen years building systems at scale, long before AI was the reason.

Payments, marketplaces, fintech and marketing platforms, as chief technology officer, VP of engineering and principal architect. The pattern is the same: high concurrency, real money or real users on the line, and no room for a system that only works in a demo.

19 yrsEngineering leadership
22.24MMonthly users served
132Engineers led, from 3
100+Technical sessions given

Paytm · Adobe · Naukri.com · PayU · Dhani · T9L

What actually goes into the build, not just the pitch.

GemmaGemma
Gemini
OpenAI
Claude
ElevenLabs
LangChain
LangGraph
ADKADK
Neo4j
PostgreSQL
MCP
Docker

03 / Cases

Knowledge graph · Industrial documentation

Years of siloed manuals, modelled as one graph.

Custom traversal algorithms rather than standard nearest-neighbour matching, built on Neo4j. Research that had taken weeks came back in seconds, and every answer could be traced.

+90%Query accuracy
-65%Response time

Ashok took years of completely siloed technical manuals and modelled them into a single high-performance Neo4j graph. Our search times dropped dramatically and research that used to take weeks now returns in seconds with full traceability.

Head of Engineering, Industrial Manufacturing Group
Voice AI · Customer support

A support engine that knows who is calling.

A custom customer support engine built on ElevenLabs and IVR. The call flow carries the caller’s real-time data and context through MCPs to the LLM, improving the quality of both the responses and the conversation.

1.6MCalls per day
7Global languages

The voice engine Ashok built completely changed our inbound support flow. By routing real-time caller context through MCPs directly to the LLM, the system actually understands who is calling and handles high daily volumes smoothly across multiple languages.

VP of Customer Operations, Enterprise Telecom Provider
Knowledge graph · Accounting

Fifteen years of books, in one queryable graph.

Books spanning thousands of clients, documented and modelled, with a query interface built on top for compliance, anomaly detection, fraud detection and forecasting.

15 yrsOf books modelled
1,000sOf clients covered

Modelling fifteen years of financial books across thousands of clients into a single queryable graph felt like an impossible task. Ashok delivered an architecture that transformed our compliance tracking, anomaly detection, and forecasting overnight.

Managing Partner, Financial Advisory Firm
Personalisation · Education

Learning paths modelled on each student.

A coaching group working mostly in physical classrooms wanted a personalised learning app modelled around each student’s strengths and weaknesses, to improve their overall learning and rank.

Per studentPath modelling
RankOutcome measured

We wanted to bring deep personalisation to physical classroom coaching without losing individual focus. Ashok modelled adaptive learning paths around each student's unique strengths and weaknesses, giving us measurable outcomes on student ranks.

Director, Test Prep Institute
Agentic AI · Manufacturing

A procurement agent that keeps the line stocked.

The agent picks up the bill of materials for new orders, in-house for future capacity and outside for client orders, then raises orders for the raw and packaging material required so stock is at capacity before production even starts.

550+Orders per week
89Vendors

The procurement agent Ashok built completely automated our raw material pipelines. It handles bills of materials effortlessly and ensures our stock is fully optimised across nearly ninety vendors before production even starts.

COO, Consumer Goods Company
Agentic AI · Logistics

Route planning for a four thousand vehicle fleet.

A route planning agent for a logistics group with a fleet of over 4,000 vehicles across four geographies. Using open map and weather data, it plans and deploys the possible routes for each shipment while keeping insurance costs to a minimum.

350k+Shipments per month
-32%Operating costs

Managing route planning for thousands of vehicles across multiple geographies used to be a massive operational bottleneck. Ashok's agent integration uses live map and weather data to lock down optimal routes while slashing our monthly operating costs.

VP of Supply Chain, Global Logistics Company

04 / How an engagement runs

Week 0

Diagnose

A call, then time with your code, your data and your team. I write down what is actually in the way, including the parts that are not technical.

Week 1 to 3

Architect

Target architecture, retrieval or graph model, evaluation plan, and a sequenced roadmap with costs against it.

Week 4+

Build alongside

I work inside your team rather than beside it. Reviews, pairing, and the difficult calls made in the open where everyone can see the reasoning.

Exit

Hand over

Documentation, runbooks, and a team that can run the system. The measure of the work is you not needing the next engagement.

He spent his first week telling us which half of our AI roadmap to cut. That single call saved two quarters of engineering time and a headcount plan we did not need.

Founder, Series A SaaS

Our retrieval accuracy had been flat for four months. He rebuilt the graph model and the evaluation harness in three weeks, and accuracy moved from the low sixties to the high eighties that same quarter.

VP Engineering, enterprise platform

He worked inside our team, not above it, and he left behind runbooks our engineers still open every week. The handover was the best part of the whole engagement.

Partner, product consultancy

06 / Next step

We may work well together.

Thirty minutes, no deck. Tell me what you are building and where it is stuck, and I will tell you plainly whether I am the right person for it.

Book a discovery call →Thirty minutes, on your calendar.