Production-Grade AI for Insurance, Banking, and Private Equity

More capacity, uncompromising speed, and strict data governance, without adding headcount.

8× faster, at a quarter of the running cost.

Recent engagement: a live AI platform serving central and investment banks was choking under scale, causing severe latency that put major client contracts at risk.

Read the full case study

Fusion AI Consulting engineers high-performance AI systems for insurance, banking, and the broader regulated finance sector. We bridge the critical gap between strategic vision and live execution. Whether that involves translating a blank-canvas concept into a deployable roadmap, or re-architecting an existing system struggling with scale, we turn operational drag into measurable velocity. We know that in highly regulated markets, innovation cannot compromise stability. That is why every architecture we deploy is built to absorb punishing real-world loads, execute with exceptional speed, maintain lean AI running costs, and embed the strict data governance and audit trails regulators demand.

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Illustration: a submission read into data, every field cited to the page it came from, and the one figure that disagrees with the schedule raised before an underwriter sees it.

Over £3bn: Processed annually by the regulated finance systems behind this experience
3 years: Engineering production systems inside a leading UK MGA and Lloyd’s syndicate
SOW or Outside IR35: Fixed-scope projects delivered end to end, or embedded contract work alongside your team

The problem

The friction costing you the most is the work nobody questions

It rarely makes a board agenda because it stopped looking like a problem years ago: chasing broker schedules, rekeying bordereaux, cross-referencing compliance data, or waiting on slow retrieval pipelines. Each task seems too small to fix. Together, they consume the week of your most expensive people.

The true cost is invisible: the submission quoted three days after a competitor, the stalled deal flow, or the AI prototype that burns thousands in cloud tokens but never reaches production speed.

Case study

Their customers had stopped waiting for answers

A data and analytics platform used by central and investment banks.

The situation

The platform answers research questions across an enormous estate of documents and data, the kind of question that would otherwise cost an analyst a day of reading. In a product like that, speed is what customers are paying for.

What was wrong

Answers were averaging thirty to sixty seconds, and the worst of them took over two minutes. Customers had moved from grumbling to formal complaints, and some had stopped asking questions altogether. From the outside there was no obvious reason why: behind each answer sat a search across a vast corpus, a chain of tool calls running one after another, and far more data than any single question needed. The client had an AI team of its own, and it had been working the problem.

What they asked for

The brief was to make it twice as fast. The client expected running costs to rise to pay for that, and had accepted it.

What was done

01Redesigned the retrieval pipeline from the ground up, because the limits were built into the design and no amount of tuning would remove them.
02Parallelised the tool calls that had been running one after another, removing most of the waiting.
03Matched the model to each task, tested against a fixed set of questions, so the most expensive model is used only where it measurably improves the answer.
04Stripped out prompt bloat, so every call carried what the task needed and nothing it did not.
05Added caching wherever the system was repeating the same work for every question.
06Introduced continuous evaluation, so accuracy and speed are measured every time the system changes, not discovered through a customer complaint.

They asked for two times. They got eight, and paid less to run it.

8× faster

Average response time

from over 2 min

Slowest response, time to first token (TTFT)

75% lower

Running cost

Measured on the client's own question set, before and after the rebuild.

For the same client we also engineered the OCR extraction pipelines and the cloud infrastructure underneath them.

In systems like this, speed, accuracy and cost are decided by the design. When a product feels slow to the people paying for it, the fix usually lies in how the work is structured, not in how much computing power sits behind it.

Two ways to engage

The engineering does not change. What changes is where it sits, and how much of your team it occupies.

Contract

Embedded delivery, outside IR35

We work alongside your engineering and transformation teams, delivering against an agreed scope rather than joining the reporting line. Best suited to shaping an AI roadmap, accelerating a build that has to land, or rescuing a pipeline that is burning budget and missing its targets. Remote first, with London days for the milestones that warrant the room.

Scoped as a statement of work, outside IR35
In your repositories, your stack, your review process
Knowledge transfers continuously, because your engineers are in it from week one

Projects

Fixed scope, delivered end to end

Turnkey delivery through Fusion AI Consulting. We map the bottleneck, engineer the system and deploy it into your environment with full documentation and handover, without diverting your people from their own roadmap.

Priced and scoped before a line is written
Proven in shadow mode against your live work before go-live
Documented, and yours to run

Talk through which one fits

Track record

Production systems across the submission lifecycle

Three years engineering inside a leading UK MGA and Lloyd’s syndicate, on the systems that sit between a submission arriving and an underwriter deciding. Not prototypes: systems carrying live volume, inside a business answerable to a regulator.

Submission triageInbox

Ranked submissions

4 new today

1
Thornbeck Cold Storage LtdProperty
via Fenwick Averill Brokers
In appetite, deadline Friday
2
Eskwith Plastics LtdCasualty
via Stannard Lowe & Partners
In appetite, renewal of a known account
3
Pelham Rudd Hotels LtdProperty
via Oakhithe Risk Services
Missing: statement of values, broker asked
4
Kirkdale Reach Marinas LtdProperty
via Marlowe Quayle Insurance Brokers
Outside appetite: occupancy

Ranked against your appetiteUnderwriter decides

Illustrative. Inbound submissions scored against appetite and ordered by what is worth an underwriter’s morning.

Inbound triage: Structured data extracted from emails and their attachments, each case routed to the team that should see it, and documents and internal databases populated without a field being retyped.
Automated risk assessment: Inbound submissions scored against underwriting appetite, so the risks worth an underwriter’s morning are the ones reaching their desk.
Entity resolution: Firm records enriched from external sources and matched across pricing and risk requests, so the same company is recognised wherever it appears.

Where this work has been delivered

Three sectors, one underlying constraint: skilled people spending their week on work a system should absorb. The detail differs from one firm to the next. The pattern rarely does.

Insurance

MGAs, Lloyd's syndicates and coverholders

Automated submission intake, appetite-based risk triage, and structured data extraction from inbound broker emails, schedules and policy documents.

What we build for insurers and MGAs

Banking and Finance

Central, investment and commercial banks

Slow retrieval pipelines re-architected, token consumption cut, and auditable OCR workflows built to stand up to review.

What we build for banks

Private Equity

Buy-side, growth equity and investment firms

Inbound deal screening against your mandate, and automated analysis across information memoranda and diligence rooms.

What we build for investors

How an engagement works

Five steps, and we carry out all five. There is no internal project team to assemble, no ticket in your IT backlog, and no point at which the work is handed back for your people to finish. Nothing touches your live operations until it has proved itself on your own work.

Discovery

We map your operational bottlenecks and measure the friction using your own metrics.

Architecture and design

We design the solution around your existing systems, defining exact data boundaries, cloud running costs, and human-in-the-loop checkpoints.

Build and deploy

We engineer the pipeline and deploy it securely into your cloud environment. Your internal team stays focused on their day job.

Shadow running

The system runs silently alongside your live operations. It processes real cases but changes nothing, proving its accuracy and speed against your team's output before it ever goes live.

ShadowProcessing real cases. Writing nothing, sending nothing.

LiveProcessing real cases. Acting, on your decision.

Go live and handover

You decide when to switch it on. We hand over the system with full documentation, backed by ongoing performance monitoring and governance.

The first conversation is free.

Leadership

Led by an engineer who builds for highly regulated environments

Fusion AI Consulting is led by Jack Quinn, who has spent his career engineering production systems in the UK MGA and London Market space, most recently at CFC, the largest independent MGA in the UK, with its own Lloyd’s syndicate. The systems he worked on there processed over £3bn a year.

Operating in environments where unnoticed failures become regulatory matters, Jack has built automated risk triage engines, document extraction pipelines for inbound submissions and bordereaux, and entity resolution systems across the submission lifecycle.

Every engagement is hands-on. Nothing fails silently, every action is logged, and AI models are used only where they are the right architectural choice rather than the newest one. The focus is always speed, unit economics and auditable governance.

About Fusion AI Consulting

What clients say

Three firms, three different problems, in their own words.

Inbound deal flow assessed against the investment strategy before it reaches a partner.

“The AI workflow Fusion AI built now handles our inbound deal flow, automatically prioritising and assessing opportunities against our investment strategy. It removed a huge amount of noise from our inbox and allows us to focus only on deals that genuinely fit. It has become part of our core operating process.”

Brad Behrens, Managing Director and Founder, Arete Capital

Document intelligence: scanned and PDF documents turned into structured data, stored, and presented with the risks flagged.

“Jack has been excellent to work with. He immediately understood both the technical requirements and the wider product vision, which made collaboration very efficient from the start. The work was delivered ahead of schedule, with strong attention to detail and a thorough handover. I'd be very happy to recommend Jack to anyone looking for a capable and reliable partner.”

Luke Watts, Founder, KAI

A recurring process that used to take hours, now running on its own.

“Fusion AI took a process that was consuming hours of our time and turned it into something that runs on its own. Jack was professional and straightforward throughout, understood what we actually needed rather than what was easiest to build, and delivered it properly. The return was immediate and we would happily work with him again.”

Malcolm Zoppi, Managing Director, Zoppi and Co

Start with a conversation

The first conversation is free. We will talk through where your team’s time is going and tell you plainly whether we can help.