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AI engineering / agents / governance

Most AI pilots die before production. Ours don't.

We design, build and govern AI systems that clear evaluation, security review and audit, then keep working once nobody is watching them. Sixteen service pillars, fourteen industries, one opinionated way of working.

Worldwide delivery · Fixed-scope pilots · Full IP transfer on final payment

16Service pillars
148Individual services
4–6wksPilot to production
100%IP transferred to you
Stack
OpenAIAnthropic ClaudeGoogle GeminiMeta LlamaLangGraphModel Context ProtocolAWS BedrockAzure AI FoundryVertex AIDatabricksPineconepgvector
In brief

What iSpark does

iSpark is an AI consulting and engineering firm that designs, builds and governs artificial intelligence systems for organisations worldwide. Work spans sixteen service pillars — from AI strategy and generative AI engineering to AI agents, machine learning, data engineering, MLOps and AI governance — delivered remotely to clients in fourteen industry clusters. Engagements are fixed in scope, a pilot reaches production in four to six weeks, and full intellectual property transfers to the client on final payment.

Founded
2026
Delivery
Remote, worldwide, in English
Engagement models
Fixed-fee diagnostic, fixed-scope pilot, milestone-based build, monthly retainer
Time to first production pilot
Four to six weeks after a two-week diagnostic
Intellectual property
Transferred in full to the client, including prompts, evaluation sets and infrastructure as code
Governance frameworks
EU AI Act, ISO/IEC 42001, NIST AI Risk Management Framework
The problem

It was never the model.

Foundation models are a commodity. What separates a convincing demo from a dependable system is everything built around it, and that is exactly where projects fail.

Failure 01

No evaluation harness

Teams ship on intuition, then find quality regressions in production with no baseline to measure against.

Failure 02

Retrieval as afterthought

Output quality is capped by retrieval quality. Most teams tune the prompt when chunking is the real problem.

Failure 03

Governance bolted on late

Risk classification retrofitted under deadline pressure, usually once legal has already started asking questions.

Failure 04

No rollback path

Agents given write access to production systems with no circuit breaker and no human checkpoint.

Axis A / what we build

Services

Sixteen pillars, 148 services. The axis a technical buyer navigates. Every pillar is a hub; every service beneath it has its own page.

01

AI Strategy & Consulting

Decide what to build before you build it. Readiness audits, roadmaps and business cases that survive a CFO review.

All 12 services →
02

Generative AI & LLM Engineering

Custom LLM applications, retrieval systems and fine-tuned models grounded in your own data, not a public chatbot with your logo on it.

All 12 services →
03

AI Agents & Agentic Automation

Agents that complete work rather than describe it, with guardrails, evaluation and an audit trail.

All 13 services →
04

Conversational AI

Chat, voice and contact centre AI that resolves the issue instead of deflecting it to a form.

All 8 services →
05

Machine Learning & Predictive Analytics

Forecasting, scoring and detection models trained on your historical data and validated against business outcomes.

All 12 services →
06

Computer Vision

Systems that read documents, inspect products and understand video in real time, at the edge or in the cloud.

All 10 services →
07

Natural Language Processing

Turn unstructured text into structure, meaning and searchable knowledge your systems can query.

All 8 services →
08

Data Engineering & AI Readiness

The unglamorous layer that decides whether your AI works at all. Pipelines, warehouses, vector stores, clean data.

All 10 services →
09

MLOps, LLMOps & AI Infrastructure

Get models into production and keep them healthy, observable and affordable once they are there.

All 10 services →
10

AI Integration & Process Automation

Wire AI into the CRM, ERP and workflows your team already uses instead of adding another tab to their day.

All 9 services →
11

AI Governance, Security & Compliance

EU AI Act, ISO 42001, red teaming and LLM security. Prove your AI is safe before a regulator or a customer asks.

All 13 services →
12

AI for Marketing & Growth

Get found by search engines and by AI assistants. Generative engine optimisation, personalisation and lead intelligence.

All 11 services →
13

AI Product & Experience Design

Design AI products people trust, with humans kept in the loop where the stakes justify it.

All 6 services →
14

Creative & Media AI

Generate image, video, voice and avatar assets at brand consistent scale, with rights and provenance handled.

All 7 services →
15

Emerging & Frontier AI

Physical AI, digital twins, AIoT and spatial computing for teams building ahead of the market.

All 7 services →
16

Managed AI Services & Talent

Embedded engineers, retainers and rescue work for AI projects that stalled between pilot and production.

All 7 services →
Where we go deepest

Two bets.

We are a specialist before we are a generalist. Two pillars carry most of our engineering time, our published research and our positioning.

Pillar 03

AI Agents and Agentic Automation

Agents that complete work rather than describe it: orchestrated, observable and safe to give real permissions to.

  • Multi-agent orchestration with explicit hand-off contracts
  • MCP servers exposing internal systems as safe tools
  • Evaluation harnesses and regression suites before launch
  • AgentOps: tracing, cost control, circuit breakers, rollback
Explore agents
Pillar 11

AI Governance, Security and Compliance

The layer legal, risk and procurement will ask about, designed in from the start rather than reconstructed later.

  • EU AI Act risk classification and obligation mapping
  • ISO/IEC 42001 and NIST AI RMF implementation
  • Red teaming and prompt injection resistance testing
  • Bias audits, model cards and explainability artefacts
Explore governance
Axis B / who we build it for

Industries

Fourteen clusters, roughly 118 verticals. The axis an executive buyer navigates, because the sector determines the data, the constraints and the regulator.

Healthcare & Life Sciences

Clinical documentation, imaging, trials and payer operations, built to survive HIPAA and clinical validation.

14 verticals →

Financial Services & Insurance

Fraud, underwriting, KYC and advisory automation inside the most heavily regulated data environment there is.

15 verticals →

Retail & E-commerce

Recommendations, pricing, demand planning and support that hold up as catalogue and order volume grow.

13 verticals →

Manufacturing & Industrial

Visual inspection, predictive maintenance and shop floor intelligence running at the edge.

13 verticals →

Technology & Telecom

AI features inside your product, plus the internal engineering leverage to ship them sooner.

10 verticals →

Energy & Utilities

Grid forecasting, asset inspection and outage prediction across distributed infrastructure.

9 verticals →

Transport & Logistics

Route optimisation, ETA prediction, freight document processing and exception handling at scale.

11 verticals →

Real Estate & Construction

Valuation models, project risk scoring and document heavy workflows.

10 verticals →

Professional Services

Contract review, research synthesis and billable hour recovery for firms that sell expertise.

10 verticals →

Public Sector & Education

Citizen services, casework automation and learning systems with auditability built in.

12 verticals →

Media, Entertainment & Sports

Content pipelines, archive search, rights management and audience intelligence.

11 verticals →

Agriculture, Food & Environment

Yield prediction, quality vision, and the ESG reporting your buyers now ask for.

11 verticals →

Travel & Hospitality

Dynamic pricing, itinerary agents and multilingual service at booking engine scale.

9 verticals →

Consumer & Lifestyle Services

Booking, lead capture and local scale automation for service businesses and franchises.

9 verticals →
How we work

Five stages. Defined exits.

Stop after any stage and keep everything produced up to that point. No stage depends on you committing to the next.

  1. 01

    Diagnose

    Two weeks. We audit your data, systems and constraints, then tell you which use cases are viable now and which are not.

  2. 02

    Prove

    Four to six weeks. One narrow pilot with a success threshold agreed before we start. If it misses, we say so.

  3. 03

    Build

    Production engineering with evaluation harnesses, guardrails and observability included rather than added later.

  4. 04

    Govern

    Documentation, model cards, risk assessment and the compliance artefacts your auditors will ask for.

  5. 05

    Operate

    Monitoring, retraining and cost tuning on a retainer, or a clean handover to your own team.

What you get in writing

Four commitments.

Every engagement is written the same way, whether it is a two week diagnostic or a year of production work.

Commitment

A price before we start

Diagnostics are a fixed fee and pilots are fixed scope and fixed price. Nothing is quoted only on request.

Commitment

A threshold we can fail

Every pilot carries a success measure agreed in advance. If the result misses it, we say so rather than reframe it.

Commitment

An exit at every stage

Stop after any stage and keep the code, the documentation and the evaluation sets produced up to that point.

Commitment

Your IP, in full

Prompts, evaluation sets, fine tuned weights and infrastructure as code transfer to you on final payment.

Who you will work with

We are new. We are not going to fake a wall of logos.

We publish how we work, price it openly, and let the engineering talk before the sales pitch does. Every engagement transfers full IP to you, including prompts, evaluation sets and infrastructure definitions.

About iSpark

Questions

FAQ

Marked up so these answers can surface in search results and inside AI assistant responses.

How quickly can we see something working?

A scoped pilot usually ships in four to six weeks. The diagnostic that precedes it takes two. We do not run six month discovery phases.

Do you work with our existing data stack or replace it?

We work with it. Replacing a warehouse to enable one AI pilot is almost always the wrong sequence, and we will tell you on the rare occasion it is the right one.

What does an engagement cost?

Diagnostics are a fixed fee. Pilots are fixed scope and fixed price. Production builds are milestone based. Indicative ranges are published rather than quoted on request.

Who owns the code and the models?

You do. Full IP transfer on final payment, including prompts, evaluation sets, fine tuned weights and infrastructure as code.

Can you help if our AI project has already stalled?

Yes. Project rescue is a defined service. We start with a two week technical audit before proposing any remediation.

Do you work with clients outside your own time zone?

Yes. Engagements are delivered remotely to clients worldwide, with overlapping working hours agreed at kick-off. Delivery, documentation, code review and training are all in English.

Which models and platforms do you build on?

We are not tied to a single vendor. We build on OpenAI, Anthropic, Google and open weight models such as Llama, deployed through AWS Bedrock, Azure AI Foundry, Vertex AI or your own infrastructure. The choice is made per use case on quality, latency, cost and data residency.

How do you handle our data and confidentiality?

An NDA is signed before any data is shared, access to your systems is least privilege and time bound, and client data is never used to train third party models. Where a use case requires it, we deploy inside your own cloud tenancy or against private endpoints.

Tell us what's stuck.

Thirty minutes. We will tell you whether your use case is viable now, viable later, or not viable at all, and we will say the third one out loud if that is the answer.