For organisations where AI isn't paying off — yet When AI isn't paying off — yet
Levantar helps you work out where AI will genuinely pay, prove it with evidence, and make it safe to rely on. Before you spend — or after you've spent and can't see the return.
Which one is you?
The gap
The difference isn't the model — it's placement, measurement and honesty about risk. That gap is why Levantar exists.
We help organisations be one of the few who get real value from AI.
AI adoption is racing ahead of returns. There's a gap in the market for a guide who has actually built and run these systems — and we work with executives, often technical, who are tired of the hype or have been burnt by it.
of organisations are adopting AI
can attribute any profit impact to it
are getting real value from it
Real value = significant value from AI plus 5%+ EBIT impact attributable to it.
Source: McKinsey, State of AI 2025
How we measure this →
Closing that gap takes three things.
Know where AI pays before you commit — and what to stop funding when it doesn't.
AI Value DiagnosticsRun systems safe enough to depend on, with the evidence of what they actually did.
AI & LLM SecurityScale what works into agents that do real work, inside controls you set.
Agentic AIWhat we won't do
We know exactly how these systems fail, because we run them: drift, silent regressions, confident wrong answers, costs that grow faster than the value. We design for those failures from the first day rather than discovering them in yours. And if the evidence says don't build, we'll tell you.
We don't sell one vendor's stack. We build and deploy adapter layers so applications use the right model for the right job at the right cost — cloud-hosted or local — and the measurement decides the tool, not a partner agreement.
What we do
01
AI Value Diagnostics
Most AI investment is committed before anyone can say what it will return.
We assess where AI will genuinely pay before you commit, and give an honest verdict on what you've already built — what to back, what to fix, what to stop funding. When the evidence says build, we guide your team through delivery, or build alongside you.
02
AI & LLM Security
AI systems fail in ways conventional security controls were never built to catch.
We assess the AI systems and LLM integrations you already run, and design new ones to be secure from the start. Either way you get the guardrails and evaluations that hold them in check, and the evidence trail — traces, evals, audit logs — that shows an auditor what your AI actually did, not what it was supposed to do.
03
Agentic AI
An agent acting inside your business is a different proposition from a chat window answering questions about it.
We design agent systems rather than single bots: which use-cases are worth automating, how work passes between agents, and what has to be proven before any of it touches something real. And we connect them securely to your own data, wherever it lives — including the systems that were never built to be queried.
Track record
Delivered personally by our founders across two decades of previous roles and engagements.
We build and run AI products of our own — in production, with our own money at stake. That experience is what we bring.
Opptora, Upcast.Social, Tripwires and Secronyx — built, run and funded by us.
Meet the companyInsights
Everything here comes out of something we built, run or delivered for a client — including the parts that went wrong.
AI Value Diagnostics
How Levantar helped Highfield, one of the UK's top five awarding organisations, decide the future of its content platform — working AI-first, with people owning every judgement.
25 Aug 2026
Read the pieceAgentic AI
Giving an agent a managed Python sandbox with AgentCore Code Interpreter, putting a file in, running code over it, and seeing what the session can and cannot reach.
21 Aug 2026
Read the pieceAI Value Diagnostics
AI budgets are being cut on cost because value is the harder number. What the 2026 cost-governance data shows, and what to prune on instead.
19 Aug 2026
Read the pieceAgentic AI
Adding AgentCore Memory to an agent so it keeps conversation events within a session and recalls extracted user preferences across sessions.
14 Aug 2026
Read the pieceAgentic AI
Six best-of-breed apps that don't talk, four small open-source MCP servers, and an agent in the middle. What we built, what broke, and what it taught us.
6 Aug 2026
Read the pieceAgentic AI
Putting AgentCore Gateway in front of an existing Lambda so an agent can call it as an MCP tool, authenticated from the first deployment.
5 Aug 2026
Read the pieceContact
The first conversation is diagnostic, not a pitch.
Tell us which problem is yours — we'll come back with the relevant next step, not a deck.