Levantes
Levantes

Production-grade AI adoption
owned and scaled by your business.

We build AI that runs inside your operation, not alongside it — governed, measurable, and scaled from a single team to the whole organisation. Translated to tangible business growth.

Start with one use case See the approach

01 / TRACKRECORD

Our clients and partners

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SME & LE

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C-suites and industry leaders

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Investment bodies and family offices

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Experts network — AI engineers, use-case experts, scientists and strategic consultants

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Scale-ups led, from seed or Series A through to Series B and exit

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World-leading academic institutions

Industries we partner with

Health tech

Health tech

Insuretech

Insuretech

Fintech

Fintech

Real estate, proptech & construction

Prop tech

Retail & e-commerce

Retail & e-commerce

Strategic communications & marketing

Strategic communications & marketing

02 / THE INDUSTRY CHALLENGE

The tools may work. The business doesn't change.

People use AI daily. The numbers that matter — capacity, cost to serve, time to answer — barely move.

Generic tools, used individually.

Nothing accumulates. Nothing is auditable. Nothing survives the person leaving.

Engineering projects, commissioned too early.

Six figures and six months before anything is proven — shipped into a process no one changed.

The actual work sits between the two: making AI part of how the organisation operates — with data it can act on and a path from one team to all of them. That stretch is where enterprise AI mostly fails. It is the stretch we work in.

03 / Measured

What we shift

+0–50%

more clients handled by the same team — no proportional hiring, in a firm where senior time was the constraint on growth

0–40%

less administrative load carried by senior people, returning their time to the relationships and judgement that justify the fee

0–25%

faster client and service research — history, prior advice, live requests, and the regulatory constraints that apply

0–60%

faster client onboarding, from signed engagement to a fully briefed team — intake documents and call notes read once and turned into a structured client profile

0–6 weeks

from the start of an engagement to a first human-in-the-loop AI adoption pipeline running on real client work

Figures come from our own client assessments, built on each firm's own numbers and benchmarked against sector data. 

04 / Business impact

Solo productivity boosts do not make a growing business

This is the part the market keeps getting wrong. Individual productivity is real, and on its own it is close to worthless commercially. A consultant who drafts twice as fast, inside a process that still routes work the same way, with a pipeline that still fills at the same rate, produces the same revenue. The gain dissipates into the day.

Productivity converts into growth only when it passes through the structure of the business — the operations, the data, the shared knowledge, the way work is routed and reviewed. That is a design problem, and it is the one we solve.

Capacity

More clients served, without proportional hiring.

Coverage

Request types you previously turned away, now in scope.

Consistency

Quality that no longer depends on who picked up the work.

Cycle time

Research, drafting and response measured in hours instead of days.

Each of these maps to a number your finance function already tracks. We agree which ones move, and by roughly how much, before anything is built — and we come back to them after.

04 / HOW WE BUILD

3-Tier Service Model

Three tiers, delivered in sequence. Each tier earns the next, each stage is a self-sufficient delivery.

Tier 1

Foundations

4–6 WEEKS
  • 01

    Use-case design

  • 02

    AI-actionable data models

  • 03

    Entry AI adoption coaching: knowledge foundation & apps

  • 04

    Where AI moves your numbers — build vs buy

Tier 2

Adoption

2-6 MONTHS
  • 04

    Solo adoption

  • 05

    Group adoption

  • 06

    Organisation adoption

Tier 3

Scale

Pattern-driven · ongoing
  • 07

    Agent orchestration and scaling

  • 08

    Turning repeated solutions into standardised end-to-end products

  • 09

    Tangible scaling of business growth

06 / Model-agnostic approach

The most expensive mistake in enterprise AI right now is a pilot that succeeded

It succeeded on a frontier model, at pilot volume, with nobody watching the invoice. Then it scaled, and the unit economics arrived.

Our framework is model agnostic by construction, not marketing. Depending on the use case, we route between custom-built and bought models — so applications stay cost-efficient as they scale, not locked into expensive off-the-shelf models.

Buy

Where the market has commoditised the capability. Paying to rebuild it is a governance failure, not diligence.

Build

Where repetition, defensibility or integration depth make it cheaper over the life of the system than renting it.

Neither, yet

Where the honest answer is that the use case has not earned a solution.

Every recommendation carries an indicative cost — including the option we didn't take.

07 / Not a black box

It starts at your use case, and it stays yours

We do not deliver a system and explain it afterwards. The use-case holder — the clinician, the analyst, the advisor, the strategist — is in the build from the first session. 

This is not a courtesy. It is the only arrangement that works. AI systems fail in the last mile, on domain judgement that no engineer and no foundation model has.

What we deliver: a system your team can command, understand test, modify, retire or scale and no dependency on us to keep it running.

08 / What holds

Command, Governance & Security

You own your intelligence and your data

Your data stays inside your boundary. The models, the data structures, the evaluation sets and the institutional knowledge we build with you are yours — during the engagement and after it. Always secured, always yours, and never leverage over you.

Governed by design

Permissioned, auditable and traceable from the first version, not retrofitted before a compliance review. Guardrails, evaluation and human sign-off are scoped in at design time, calibrated to what the use case actually risks.

Model-agnostic by construction

No vendor lock-in — including none to us. When the economics or the capability shift, the model underneath changes and the system stays.

Understand & command

Every system is documented, testable and owned by the people who use it. 

09 / Selected work

We work across industries

Health tech · Insuretech · Fintech · Real estate, proptech & construction · Retail & e-commerce · Strategic communications & marketing · Venture capital & investment · Professional & corporate services

01

AI adoption & transformation

Marketing & strategic communications · B2B thought leadership

AI adoption: from prompting to infrastructure

Challenge. A boutique marketing firm was already using AI daily and had hit a ceiling it could not see. Context and research were re-entered by hand every session, brand voice drifted between people, and cost multiplied with every client. Nothing accumulated.

What we did

  • Measured where fee-earner time actually went, using the firm's own numbers.
  • Built a structured client data model and campaign automation platform.
  • Sequenced the first build so it doubled as the foundation for the next.

Impact. Per fee earner, two days a week had been going to work that repeated — more than a full-time equivalent of capacity across a small team. The first automated pipeline ran on real client work within 4–6 weeks.

2 days a week saved

Press briefing and media analytics for strategic communications

Asset & lifestyle management · UHNW

Boosting research and communications capacity

Challenge. An asset and lifestyle management firm where human capital is the product, and senior employees were spending their time on administrative coordination rather than the relationships that justified the fee. Growth meant hiring, and hiring at that level of discretion is slow.

What we did

  • Opened with an ROI framework rather than a build.
  • Mapped eight benchmarked return categories against concrete AI adoption steps.
  • Applied agentic adoption to communications, research and coordination, with people retaining judgement throughout.

Impact. The same team could handle 30–50% more clients, with 25–40% less administrative load on senior staff.

+30–50% client capacity

Polished marble and brass still life suggesting private wealth management

02

AI product & solutions engineering

Health tech · Digital mental health

An end-to-end online platform for mental health support

Challenge. Clinical services needed to reach more patients without diluting quality of care or the clinician's judgement — a domain where a wrong output is a safeguarding matter, not a bad draft.

What we did

  • Delivered end-to-end product engineering and data engineering as one programme.
  • Built agentic tooling supporting clinicians directly, and patients alongside them.
  • Led the company's innovation trajectory and growth strategy.

Impact. The platform carried the company through its full funding trajectory to acquisition.

Series A → B → exit

Calming clinical therapy room with natural light and plants

Retail & e-commerce · Personalisation

Personalised styling, built on the retailer's own data

Challenge. A company holding purchase data from several leading UK and US retailers had no way to turn it into something a customer would feel. It needed an automated styling capability that could sit inside its clients' existing platforms.

What we did

  • Converted a proprietary database into an AI-ready training platform.
  • Built customer profiling across style, attributes and buying behaviour, then recommendation on top.
  • Made it learn continuously from incoming customer and retailer signals.

Impact. Customers found what they wanted faster, while retailers gained a lever on seasonal sales and annual retention they had not had before.

Two commercial goals served at once

Bright boutique retail interior

Insuretech · Dynamic pricing

Dynamic insurance pricing for Type II diabetes

Challenge. The company wanted automated, personalised pricing of health insurance policies. The obstacle was not the algorithm — it was that the data required to price the risk had never been collected.

What we did

  • Designed the clinical use case and the data-acquisition framework.
  • Delivered an end-to-end disease prediction engine.
  • Staged delivery and testing across use cycles with patients and clinical supervisors.

Impact. A data-science and AI asset most companies reach years later, with IP that opened a route into automated pricing for health insurance.

Defensible from pre-seed

Diabetes risk data and health-insurance assessment on a clinical tablet

Insuretech · Claims automation

Damage assessment automation

Challenge. A first analytics capability for automated vehicle damage evaluation, in a business where the exception is not an edge case — it is the claim that costs money.

What we did

  • Built the training corpus from scratch through data scraping and engineering.
  • Moved from binary recognition to categorised, severity-quantified assessment.
  • Changed data capture so the asset would keep compounding rather than decay.

Impact. Accuracy held against arbitrary client-supplied datasets, not a curated benchmark.

>85% accuracy in AI-driven damage evaluation

Vehicle damage detected by computer vision overlays

Fintech · Wealth & asset management

Making scattered asset information actionable

Challenge. A wealth venture serving family offices and high-net-worth individuals relied on analytics across asset information scattered between spreadsheets and scanned leases and contracts. Without a structured model underneath, no analytics were possible.

What we did

  • Designed the model connecting clients, wealth, goals, assets, prices, locations and leases.
  • Specified an ingestion engine using language models to read contracts beyond basic text recognition.
  • Scoped feature engineering, analytics, decision support and valuation for assets such as art and cars.

Impact. The defining finding was that the analytics ambition was unreachable until the data model existed beneath it. Setting that strategy early shaped the product rather than delaying it later.

AI data actionability & investment secured at seed stage

Wealth and asset information organised into an analytical data interface

03

AI actionability & investment strategy

Architecture, engineering & construction · Proptech

AI roadmapping for topological data applications

Challenge. A company with 3D topological data reconstructed from 2D imagery, an AI roadmap layered on top of its core visualisation product, and investors who needed to know whether either was real.

What we did

  • Assessed how the data was captured, represented and stored; it was genuinely AI-ready.
  • Tested the roadmap against the research difficulty it actually implied.
  • Proposed data and AI actionability framework for next steps 

Impact. We rewrote the implementation plan into a lean sequence and specified the profiles they needed to hire, protecting both the investment decision and the company's ceiling.

Product & AI-actionability plan in place & investment decision protected

Construction site at golden hour

Proptech · Seed stage

Automating digital property sales

Challenge. A digital property sales platform connecting developers, buyers and agents, with an ambitious set of AI use cases. The question was whether the data underneath could carry any of them.

What we did

  • Interviewed team on the use cases behind the vision.
  • Interviewed the technical team on what the data actually contained.
  • Established whether third-party data was planned to close the gap. It was not.

Impact. IMPACT. The newly captured data could support the infrastructure of vision. 

Data actionability and use case achieved - Investment secured

Residential buildings at dusk linked by glowing data lines

Venture capital · Standardising portfolio practice

Standardising innovation due diligence at scale

Challenge. London funds facing AI propositions they could not evaluate from the pitch deck, in a market where valuation hype was outrunning technical substance.

What we did

  • Built a standing assessment across use-case soundness, feasibility, data usability, IP defensibility and team capability.
  • Delivered findings that changed investment decisions in both directions.
  • Convened 14 UK venture funds into a working group to counter valuation hype.

Impact. Funds came together around actionable AI roadmaps, recruitment and product design, countering hype with method.

14 UK VC Alphagroup Innovation - DD Standardisation roadmap to combat valuation hype

Abstract glass growth chart

10 / TEAM

Who we are

Levantes is a network of more than 70 senior AI engineers, strategic consultants, and use-case experts with decades of combined experience building and shipping production systems — coaching C-suites, investors, and decision makers along the way.

Our work spans health tech, insurance, finance, property and construction, retail, strategic communications, investment and professional services — the verticals below are where our people have trained, researched and delivered.

Audatex
Kredirel
Suits Me
Sensat
Allmyhomes
My Online Therapy
RHV
1FS Wealth
Round Hill Capital
Institutions and organisations our team has trained, researched or worked with

11 / Start small

One use case, scoped tightly, before anything else

Most engagements open the same way: a short, fixed-scope assessment of where AI moves your numbers, in what order, with build-versus-buy reasoning and indicative cost against each opportunity. Small enough to approve without a committee. Useful even if nothing follows it.

From there the shape is yours to choose — a proof of value in weeks, a full adoption programme across a function, or standardised systems once repetition has earned them. You are never asked to commit to the last step to get the value of the first.

Start with one use case Fixed scope · fixed cost