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Nishkarsh Jain

Work

Data & AI work that survives contact with a business case

I work with enterprise teams and with the companies selling to them — designing the platform, building the proof, and making the commercial argument hold together.
Discuss a projectSee what I do
Regions
United States · United Kingdom · APAC
Sectors
Banking & Financial Services · Insurance · Healthcare · Life Sciences · Manufacturing · Retail · Consumer Goods · Technology

Selected impact

What this has looked like in practice

Client names and commercial terms stay private. The method, the architecture and the measured result do not.

Showing 3 of 3 case studies.

Leadership

Scaling a Solutions Engineering function without losing craft

Enterprise Data & AI Services

Result
2→12

The situation

A fast-growing data and AI services organisation was expanding its customer-facing solutioning capability across regions and industries.

Growth was outpacing shared process. Quality depended too heavily on individual experience, slowing collaboration across sales, delivery and engineering.

What I did

Helped build and scale the Solutions Engineering organisation while staying close to discovery, architecture, proposals, executive demos and live deals.

  • Codified discovery and qualification
  • Created reusable estimation and proposal patterns
  • Built repeatable demo environments
  • Coached team members on technical storytelling

What came of it

  • Function scaled from 2 to 12
  • More consistent solutioning across opportunities
  • Reduced sales-cycle friction by approximately 1–2 weeks
Read the full case study
Enterprise GTM

Turning technical evaluation into an enterprise decision

Multi-industry Enterprise GTM

Result
78%

The situation

Enterprise opportunities involved technical, commercial and executive stakeholders with different definitions of value and risk.

The solution had to be technically credible while remaining anchored to the business decision—not a feature tour.

What I did

Led or supported discovery, solution architecture, POCs, RFP/RFQ responses, estimation, demonstrations and implementation roadmaps.

  • Identified the buyer's highest-risk assumptions
  • Mapped capabilities to measurable outcomes
  • Designed tailored demonstrations
  • Created decision-ready technical narratives

What came of it

  • Achieved a 78% deal conversion rate
  • Supported multiple $1M+ enterprise opportunities
  • Built credibility across US, UK and APAC stakeholders
Read the full case study
Automation

Compressing weeks of solution estimation into a day

Enterprise Data Services

Result
~1 day

The situation

Complex data engagements required granular effort, resource and cost estimates before proposals could move forward.

The manual workflow took approximately 1.5–2 weeks and created avoidable back-and-forth between customer-facing and delivery teams.

What I did

Designed the estimation logic, translated it into a guided application and aligned the output to proposal and review workflows.

  • Decomposed estimation into reusable rules
  • Built guided inputs and validations
  • Automated resource loading and cost calculations
  • Produced review-ready outputs

What came of it

  • Reduced turnaround from 1.5–2 weeks to around 1 day
  • Made assumptions visible and reviewable
  • Improved reuse across solutioning work
Read the full case study

A note on what is not here

A significant amount of my work sits under NDA and will never appear on a public website — banking modernisation programmes, competitive enterprise evaluations and internal tooling among them. If a specific sector or problem shape matters to you, ask me directly and I will share what I am able to.

Expertise

Six places where I tend to be most useful

Every engagement starts from a commercial question, not a technology preference. These are the six shapes that question usually takes.

Data & AI Strategy

A sequenced plan tied to commercial value

The problem
Most organisations have more data initiatives than they have conviction — roadmaps written around tools rather than around decisions.
What you get
A twelve-month plan where every workstream has a sponsor, a value range and a first proof point.
How I do it

I run structured discovery with the people who own the numbers, map the current data and decision landscape, and rank opportunities by value, feasibility and time-to-proof. The output is a sequenced roadmap with named owners, not a slideware vision.

  • Current-state data and decision map
  • Opportunity backlog scored on value and feasibility
  • Sequenced roadmap with owners and milestones
  • Investment and capability model

Modern Data Platforms & Lakehouse Architecture

Architecture that holds up under real workloads

The problem
Platform decisions made in a hurry become the constraint everyone works around for five years — and a warehouse nobody quite trusts.
What you get
A blueprint your engineers can build against, with cost, data contracts and migration sequencing settled up front.
How I do it

I design target architectures on Snowflake, AWS and Azure using lakehouse and medallion patterns, define modelling and dbt transformation standards, and set governance, cost and reliability guardrails before the first pipeline is written.

  • Target-state architecture and data flow diagrams
  • Ingestion, modelling and transformation standards
  • Cost, performance and governance guardrails
  • Migration and cutover sequencing

Generative AI, RAG & Agentic Prototypes

From plausible demo to defensible system

The problem
Generative AI pilots stall between an impressive demo and something a risk committee will sign off on.
What you get
A prototype stakeholders can use, an honest evaluation of where it fails, and a costed path to production.
How I do it

I build working RAG, embedding and agentic prototypes against your real content — then design the evaluation harness, retrieval strategy, guardrails and human-in-the-loop steps that decide whether the thing survives contact with production.

  • Working prototype on representative data
  • Retrieval, chunking and vector-store design
  • Evaluation harness and quality baseline
  • Cost model and productionisation plan

Enterprise POCs & Executive Demonstrations

Proof that moves a decision forward

The problem
Proofs of concept quietly consume quarters, proving the technology works while never answering what the buying committee asked.
What you get
A time-boxed proof, a demo that survives hard questions, and a decision at the end of it.
How I do it

I scope POCs around a single decision, define success criteria with the sponsor before we start, build the narrative and the environment together, and rehearse the executive walkthrough so the technical story and the commercial story land as one.

  • Success criteria agreed with the sponsor
  • Reusable demo environment and dataset
  • Executive narrative and walkthrough script
  • Findings pack and recommendation

Solutions Engineering & Presales Transformation

Turning heroics into a repeatable function

The problem
Presales teams run on individual heroics: quality varies by person, and the same architecture is redrawn for every opportunity.
What you get
Consistent quality whoever runs the deal, and measurably shorter cycle times.
How I do it

I build the operating system for the function: discovery frameworks, qualification criteria, estimation models, proposal and architecture templates, reusable demo assets, and the coaching rhythm that makes them stick.

  • Discovery and qualification framework
  • Estimation model and reusable templates
  • Demo asset library and reference architectures
  • Enablement and coaching cadence

GTM Enablement & Fractional SE Leadership

Senior presales leadership, without the headcount

The problem
Growing data and AI companies need senior solutions leadership long before they can justify the hire. The gap shows up as lost deals.
What you get
Enterprise-ready GTM from week one, and a permanent team set up to carry it.
How I do it

I embed part-time as your solutions leader: pairing with sales on live enterprise opportunities, shaping the technical narrative, running executive conversations, and hiring and coaching the team that eventually replaces me.

  • Hands-on support on live enterprise deals
  • Technical positioning and messaging
  • Hiring scorecards and onboarding path
  • Coaching for in-house SEs

How I work

Five steps, each one earning the right to the next

Nothing here is unusual. What makes it work is that every step has a commercial purpose as well as a technical one — and neither is allowed to run ahead of the other.
  1. 01

    Discover

    Typically 1–2 weeks

    Understand the decision the business is trying to make faster, who owns it, and what a good outcome is worth.

    Interviews, data-flow mapping, and an honest read on data quality and delivery capacity.

  2. 02

    Define value

    1 week

    Convert the opportunity into a value hypothesis with a range, a sponsor and a measurable success criterion.

    Options with trade-offs, effort estimates from a reusable model, and the architecture each implies.

  3. 03

    Prototype

    2–4 weeks

    Give stakeholders something they can react to before the budget conversation, not after it.

    A working build on representative data, kept deliberately narrow.

  4. 04

    Prove

    1–2 weeks

    Test the hypothesis against the criteria agreed in step two, and put the result in front of the people who sign.

    Evaluation against the baseline, cost and security review, and an executive walkthrough.

  5. 05

    Scale

    Ongoing or advisory

    Hand over something the organisation can run and extend without me in the room.

    Production architecture, delivery roadmap, standards, and enablement for the in-house team.

Bars are to scale. The lighter extension is the gap between the shortest and longest a step takes; Scale is open-ended, so it is dashed rather than measured.

Engagement models

Three ways this usually works

If none of these fit, say so in the first conversation — the shape should follow the problem.

Advisory retainer

A standing few days a month for architecture review, roadmap decisions and second opinions on vendor and platform choices.

Best when you have a team and need judgement, not hands.

Fixed-scope project

A defined piece of work with an agreed outcome — a strategy, a target architecture, a prototype, or a proof of concept run to conclusion.

Best when the question is specific and the timeline matters.

Fractional SE leadership

Embedded part-time as your solutions leader: live enterprise deals, technical narrative, hiring and coaching the permanent team.

Best for data and AI companies moving upmarket.

A worked example

What “from demo to defensible” actually looks like

The path a generative AI prototype takes before anyone signs for it. Step four is the one that gets skipped, and it is the reason most pilots stall.

  1. Grounded corpus

    Your real content, chunked and embedded — not a sample set.

  2. Retrieval + generation

    Vector store, reranking and the prompt contract around it.

  3. Evaluation harness

    A scored baseline, so “better” becomes a number.

  4. Guardrails + review

    Human-in-the-loop, security and governance sign-off.

  5. Cost model

    Per-interaction economics at production volume.

The same sequence runs for a platform migration or a POC — only the artefacts change. Every step has an owner and an exit criterion agreed before it starts.

Technology landscape

The tools, grouped by the job they actually do

A logo wall says very little. This is the same information organised the way an architecture conversation goes.

Data platforms

Where the data lands, is modelled and is served.

  • Snowflake
  • Lakehouse & medallion patterns
  • Dimensional modelling
  • Data warehousing
  • Data quality & contracts

Cloud

The infrastructure the platform runs on.

  • AWS
  • Microsoft Azure
  • Storage & compute design
  • Cost & performance optimisation
  • Security and access patterns

AI & application development

Turning models and data into something people use.

  • Generative AI
  • Retrieval-augmented generation
  • Agentic workflows
  • Embeddings & vector databases
  • Python
  • Streamlit

Analytics & transformation

Making numbers trustworthy and repeatable.

  • SQL
  • dbt
  • Pipeline orchestration
  • Semantic layers
  • BI enablement

Solutioning & integration

How systems, teams and commercial processes connect.

  • REST APIs
  • Solution architecture
  • Estimation & effort modelling
  • RFP / RFQ response
  • Reusable demo environments

Certifications

  • SnowPro Core CertifiedSnowflake
  • Azure Data Scientist AssociateMicrosoft

Bring the problem, not a brief.

Thirty minutes is usually enough to work out whether there is something worth doing here, and whether I am the right person to do it.

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