Book a call
AI transformation consultancy · UK → worldwide

Making AI actually work inside your company.

Strategy, engineering, and adoption from one accountable partner. I help leadership teams take AI from boardroom ambition to production systems: autonomous agents, agentic workflows, and LLM and small-language-model products that deliver measurable results.

Book a discovery call Explore services ↓
20+ years in technology Formerly Facebook UK base, global clients Strategy through delivery
The problem

Most AI initiatives stall between the pilot and the P&L

Industry studies consistently find that most enterprise AI projects never reach production value. The pattern is nearly always the same, and it's rarely the model's fault.

01

Strategy without architecture

A vision deck with no target architecture, no data plan, and no costed path to production. Impressive in the boardroom; unbuildable in the sprint room.

02

Pilots on brittle foundations

GenAI demos wired to legacy systems, manual deployments, and data nobody trusts. The pilot works; scaling it collapses under infrastructure debt.

03

Tools without adoption

Licences bought, copilots switched on, and workflows unchanged. Without process redesign and enablement, AI spend becomes shelfware.

Services · 8

Eight practice areas, one accountable partner

From a two-week strategic assessment to a multi-quarter transformation programme, designed so strategy, engineering, and adoption move as one. Tap any service to expand.

A rigorous, commercially grounded view of where AI creates value in your business (and where it doesn't), translated into a sequenced roadmap your board can fund and your engineers can build.

RoadmappingBusiness casesOperating modelVendor strategy
  • AI opportunity & readiness assessment across functions
  • Value-case modelling: cost-out, revenue, and risk quantified
  • Build / buy / partner decisions and vendor evaluation
  • Operating-model design: teams, skills, ways of working
  • Executive and board alignment sessions

Production-grade agentic AI, not demos. I design and build autonomous agents and LLM applications with the skills, memory, evaluation, and guardrails needed to run them safely in front of customers and staff.

Claude / GPT / LlamaAgent frameworks & MCPBedrockVector DBs
  • Autonomous agents: planning, tool use, memory & skills libraries
  • Agentic workflows: multi-agent orchestration & human-in-the-loop
  • RAG architectures: chunking, embeddings, vector search, re-ranking
  • Small language models: fine-tuning & distillation for private, low-cost inference
  • Evaluation harnesses, red-teaming, hallucination controls
  • Cost & latency optimisation: caching, routing, model tiering

From concept to shipped product: defining, prototyping, and delivering AI-powered products and features your customers adopt and retain, with delivery leadership through to launch.

0→1 buildsMVP → scaleProduct architecture
  • Use-case discovery & rapid prototyping
  • Product architecture & UX patterns for AI
  • Roadmap, team shape & delivery cadence
  • Launch, instrumentation & iteration loops

The data layer that makes AI trustworthy: modern pipelines, governed access, and the MLOps discipline to ship models with the same rigour as software.

SageMakerDatabricksdbt / AirflowFeature stores
  • Lakehouse & streaming data architectures
  • Training & fine-tuning pipelines for small language models
  • Feature stores, model registries, CI/CD for ML
  • Monitoring, drift detection & retraining loops
  • Data quality, lineage & access governance

Well-architected AWS estates designed for AI workloads: secure, cost-efficient, and ready to scale from pilot to platform.

EKS / ECSServerlessWell-ArchitectedFinOps
  • Landing zones, networking & multi-account design
  • Migration & modernisation of legacy estates
  • GPU & inference infrastructure for AI workloads
  • FinOps: cloud cost visibility & optimisation

The delivery engine underneath every successful AI programme. I modernise how your teams ship software, so AI features move from idea to production in days, not quarters.

TerraformGitHub ActionsKubernetesDatadog / Grafana
  • CI/CD pipelines, trunk-based development, GitOps
  • Infrastructure as Code & policy as code
  • Observability: logging, tracing, SLOs, incident response
  • Internal developer platforms & golden paths

Where most near-term ROI actually lives: embedding AI into how your organisation works, from engineering copilots to autonomous agents running operations, sales, and support workflows.

Copilots & agentsAgentic automationSkills librariesChange management
  • Tool selection & rollout: coding assistants, agents, automation
  • Agentic workflow design: which tasks agents own, which stay human
  • Reusable skills & playbooks so agents improve with every use
  • Champions programmes, training & capability building
  • Usage analytics: measuring real productivity impact

Move fast without breaking trust. Guardrails that regulators, customers, and your board increasingly expect, designed to enable delivery rather than slow it down.

EU AI ActISO 42001NIST AI RMFSecOps
  • AI risk frameworks aligned to EU AI Act, ISO/IEC 42001, NIST AI RMF
  • Model & data governance: access, lineage, retention, PII
  • LLM security: prompt-injection defence, output filtering, isolation
  • Responsible-AI policy, review boards & audit readiness
Capability stack

Full-stack fluency, from boardroom to bare metal

AI transformation fails when strategy and infrastructure are handled by different people who don't speak each other's language. I work across every layer, and I connect them.

Layer 04

Experience & adoption

Where AI meets people: customer-facing products, internal copilots and autonomous agents, and the redesigned workflows that turn capability into productivity.

AI product UXCopilots & autonomous agentsAgentic workflowsEnablement
Layer 03

Intelligence

The models and orchestration doing the reasoning: autonomous agents, agentic workflows, LLM and SLM applications, RAG systems, and classical ML, with evaluation and guardrails built in.

Autonomous agentsLLMs & SLM fine-tuningRAG & vector searchAgent skills & toolsEvals & guardrails
Layer 02

Data

Trustworthy, governed, AI-ready data: modern pipelines, lakehouse architectures, and the quality and lineage controls that make model outputs defensible.

LakehouseStreaming & ETLFeature storesGovernance & lineage
Layer 01

Infrastructure & delivery

The foundation everything runs on: well-architected AWS, Kubernetes, infrastructure as code, and the CI/CD and observability discipline of a high-performing engineering org.

AWS / EKSTerraform / IaCCI/CD & GitOpsObservability & SRE
Ways of working

Engagement models built around your stage

Model A

Strategy sprint

A 2 to 4 week intensive: readiness assessment, opportunity map, target architecture, and a costed roadmap. Everything you need to make an investment decision.

Best for: leadership teams at the start of the journey
Model B

Advisory retainer

Ongoing counsel for your executive team: architecture review, vendor decisions, hiring support, and a sounding board with no agenda except your outcome.

Best for: teams executing with in-house capability
Model C

Delivery partner

Hands-on leadership of a build: I own an outcome end-to-end, working with your engineers or trusted partners from architecture through to production.

Best for: flagship AI products & platform builds
Model D

Fractional CPO / CAIO

Senior technology leadership, part-time: I take accountability for your technology and AI agenda while you build the permanent team.

Best for: scale-ups and PE-backed businesses
Method

A delivery pipeline, not a slide pipeline

Phase 01

Diagnose

Audit of your systems, data estate, and delivery pipeline. Workflow decomposition to identify which tasks are ready for agents, which need human-in-the-loop, and which stay manual.

Deliverables
  • AI readiness scorecard
  • Agentic opportunity map
  • Data & infra gap analysis
Phase 02

Architect

Target-state design: agent architecture, model strategy (frontier LLM vs fine-tuned SLM per workload), orchestration and tool-use patterns, security boundaries, and cost model.

Deliverables
  • Reference architecture
  • Model & vendor selection
  • Costed 12-month roadmap
Phase 03

Build

Production engineering of the first slice: agents with tool use, memory, and skills; RAG pipelines; CI/CD and infrastructure as code from day one. No throwaway prototypes.

Deliverables
  • Production agent v1
  • Eval harness & guardrails
  • IaC & deployment pipeline
Phase 04

Scale

Hardening for enterprise load: observability and tracing across agent runs, drift and regression monitoring, cost and latency optimisation, multi-team rollout patterns.

Deliverables
  • Observability stack
  • SLOs & incident runbooks
  • Governance controls
Phase 05

Embed

Capability transfer so your organisation runs it without me: skills libraries and playbooks agents improve with, team training, and metrics wired to business outcomes.

Deliverables
  • Skills library & playbooks
  • Team enablement programme
  • Value-tracking dashboard
About

Saurabh Doshi

Saurabh Doshi

Twenty years building and transforming technology organisations, including at Facebook, where I learned what it takes to run products and infrastructure at global scale.

Today I work independently with companies worldwide on one problem: making AI real. Not pilots that impress and expire, but strategy connected to architecture, models connected to data, products connected to infrastructure, and all of it connected to how your people actually work.

I sit comfortably in two rooms most consultants choose between: the boardroom, where AI has to make commercial sense, and the engineering review, where it has to make technical sense. My job is making sure those two rooms agree.

Based in the UK, working with clients globally. Remote-first, on site when it matters.

Experience
20 years in technology
Background
Ex-Facebook
Base
United Kingdom · global clients
Focus
AI transformation, end to end
FAQ

Common questions

How do engagements usually start?

Almost always with a strategy sprint or focused assessment: two to four weeks that produce a readiness view, an opportunity map, and a costed roadmap. It's a low-risk way for both of us to test the fit before committing to a bigger programme.

Do you work hands-on or advisory-only?

Both, and often in sequence. I'm comfortable writing architecture documents for a board and equally comfortable in the codebase, the Terraform, and the CI pipeline. Most clients value that the person who set the strategy is accountable for it surviving contact with production.

We're not on AWS. Can you still help?

Yes. AWS is where I have the deepest infrastructure experience, but the architecture principles (landing zones, IaC, CI/CD, observability, MLOps) transfer across Azure and GCP, and most of the AI layer is cloud-agnostic by design.

How do you price?

Fixed price for sprints and assessments; monthly retainers for advisory and fractional roles; and outcome-scoped statements of work for delivery engagements. You'll always know the number before we start.

Do you work with our engineering team or bring your own?

Your team, wherever possible. Capability transfer is the point. Where you have gaps, I can bring trusted specialists or help you hire, but the goal is always that your organisation can run what we build.

What about data security and confidentiality?

Every engagement runs under NDA. Architecturally, I default to your tenancy and your controls: private model endpoints, no training on your data, least-privilege access, and full audit trails. Governance isn't an afterthought. It's one of my practice areas.

Contact

Let's find your highest-value AI move

A 30-minute call, no obligation and no pitch deck. Tell me where you are with AI; I'll give you a straight view on where I'd start, what it would take, and whether I'm the right person to help.

Email saurabh@saurabhdoshi.com Book a call directly
LinkedIn ↗ Back to top ↑