Alexis EVO
Portrait of Alexis EVO

Alexis EVO

AI Solutions Engineer. Co-founder of Navire. I run discovery with clients, build LLM-powered proofs of concept, and deploy them in production, including in regulated, data-sensitive environments where every threshold has to survive an audit.

Built and shipped an on-premise data-privacy suite now live in 25 French accounting firms, cutting a weekly re-keying task from two or three hours to a 10-minute review. Six years across AI delivery and client-facing consulting in Paris, London and remotely, including two and a half years at Onfido shipping releases with engineering and ML teams. Currently also retained by a London multi-family office as sole technical lead on its sanctions and PEP screening, with the thresholds documented for external audit. I own the full solutions cycle: discovery workshop, proof of concept, production deployment, handover.

Based in APAC · Malaysia, UTC+8 · French native, fluent English

What I do

Discovery to production

I run discovery workshops with client leadership, then deliver a scoped, time-boxed proof of concept within two weeks. Every production deployment I have shipped started as a paid PoC, and ends with a handover the client owns.

Discovery and operational audit · pre-sales and solution scoping · workshop facilitation · stakeholder management · runbooks and training

LLM and agent systems

Agents that do a job rather than demo one: deterministic parsing first, LLM calls reserved for the cases rules cannot handle, retrieval that returns the right passage, and tooling the model can actually call.

Anthropic and OpenAI APIs · open-weight models, self-hosted · agentic workflows · multi-agent orchestration · RAG · MCP (published open-source server)

Regulated and data-sensitive environments

On-premise and local-first deployments where client data never leaves the machine. Where the output has to be defended to a regulator, the thresholds and the failure modes get documented alongside it.

Pseudonymisation before any LLM call · AML / KYC screening · sanctions and PEP data (OpenSanctions, OFAC) · Companies House PSC API · MLR 2017 record-keeping · GDPR and data protection by design · audit-facing documentation

Evaluation and calibration

A model that is not measured is not shipped. Frozen test sets, scoring rubrics and regression checks on every release, and thresholds calibrated on the client's own logged history rather than guessed.

Rubric-based scoring · LLM-as-judge · frozen test sets · regression detection · threshold calibration and precision/recall trade-offs on production decision systems

Every engagement runs the same way: discovery, proof of concept, production, handover. You own the system at the end, with no dependency on me.

Things I've built

Product · on-premise · live in 25 firms

Navire.ai: LLMs on sensitive documents, GDPR-safe

An on-premise data-privacy suite for accounting firms. It pseudonymises sensitive client data (names, company registration numbers, bank details) before any LLM call and restores it in the response, so firms use AI assistants on confidential files without data ever leaving their machines. Live in 25 French firms. Demo above.

The full story

The situation. Accounting firms wanted AI assistants on client files, but every file carries names, registration numbers and bank details that cannot be sent to a third-party model. The manual alternative, re-keying bank exports into statutory format, took two or three hours a week per client file.

What was built. A pseudonymisation layer that swaps sensitive fields for encrypted aliases before anything reaches the model and restores them in the answer. Behind it, an agentic document pipeline: raw bank exports in, validated entries in French statutory accounting format out. The pipeline is split between deterministic parsing and LLM calls reserved for the cases the rules cannot handle.

The result. Re-keying went from two or three hours a week to a 10-minute review per client file. Token spend fell by roughly 70%, and costs stay predictable because everything runs on the firms' own hardware. Live in 25 firms.

Regulated · client

KYC screening and onboarding for a London family office

Sole technical lead on sanctions and PEP screening and client onboarding at Waverley Private Asset Partners, a multi-family office holding UHNW families through trust and corporate structures. Replaced a manual process that had no threshold calibration and no entity resolution with an engine screening against UK and OFAC sanctions data, OpenSanctions PEP lists and the Companies House PSC API. Every threshold written down for external audit. Runs on the firm's hardware, so no client document leaves the perimeter.

The full story

The situation. Onboarding a single family meant reading trust deeds, articles, share registers, foreign registry extracts and source-of-wealth evidence by hand, then screening whoever that reading turned up. Standard investment files took two committee cycles to clear. The incumbent screening process had no threshold calibration, no entity resolution, and matched names by eye. Vendor tools were evaluated first: none resolved trust structures (settlors, trustees, beneficiaries, controllers and underlying corporate ownership) into a single screened entity per client structure, which was the firm's core requirement. So it was built in-house.

What was built. A document pipeline that returns structured fields with a confidence score, each routed to a compliance reviewer, because KYC requires a person to sign off every field. Retrieval happens at page level before extraction rather than pushing whole documents at the model, which is where most of the accuracy came from. The same index answers questions over the deed corpus in natural language and cites the source clause. On top of it, each family's holding structure is reconstructed as a graph — settlor, trustee, protector, beneficiaries, underlying companies, Companies House PSC data — and resolved into one screened entity set. The PSC register is treated as the unverified self-reported source it is: corroboration rules, documented failure modes, and no direct-owner entry accepted as a beneficial owner on its own.

Calibration, not guesswork. The engine screens against UK and OFAC sanctions data, PEP lists (OpenSanctions) and the Companies House PSC API. Match thresholds were set by above-the-line / below-the-line testing against the firm's own logged history: roughly 3,000 client and counterparty checks spanning two years, replayed and scored against the incumbent process. The recall condition was fixed up front — every standing PEP and sanctions match on the client book preserved, and no alert previously escalated by compliance allowed to auto-clear. Independently validated before go-live.

Where automation stops. Agreed in the same forum as the thresholds and documented for internal compliance sign-off and external audit: suspicious activity reporting kept out of scope entirely; beneficiary distributions, tax and succession excluded; low-confidence extractions and rejections go to mandatory human review and are never auto-declined. Record-keeping meets MLR 2017 and HMRC Trust Registration Service duties.

The result. Alerts requiring manual review down by about a third. Median file-open-to-decision time roughly halved since go-live, with standard investment files clearing in one committee cycle instead of two. The pipeline was triaged into the compliance workflow already in use rather than run alongside it, with runbooks written and the internal team trained; it now drives the annual review of the existing book as well as new files.

Illustration of one AI sales agent connected to voice, chat, Telegram and WhatsApp channels
Agentic AI · client

AI sales agent, four channels, one pipeline

One Claude-powered agent behind voice, website chat, Telegram and WhatsApp: shared lead records, shared RAG knowledge base, shared scoring, plus fully automated outbound calling. Built for a B2B sales agency.

The full story

The situation. Four channels handled by four disconnected tools. The same lead could call and message on Telegram without anyone connecting the two. Outbound still meant a human dialing through a spreadsheet.

What was built. Thin channel adapters feeding one shared agent. Outbound campaigns dial through Retell AI and adjust lead scores from post-call sentiment. A 53 KB embeddable widget drops onto any website with one script tag.

The result. 4 channels unified in one pipeline, outbound qualification with no SDR involvement, live in partner testing across 7 Docker services with multi-tenant access control.

Illustration of three calendars audited automatically with zero manual review
Automation · client

Calendar SOP enforcement, zero manual review

A serverless n8n system that audits three Google Calendars against strict meeting rules every 5 minutes and posts deduplicated alerts to Slack. Built for a US investment fund.

The full story

The situation. Protected mornings, internal-only and external-only days, buffers, daily caps. Enforcement was a tedious daily human review, often late, easy to miss.

What was built. A 5-minute polling audit engine with a Supabase-backed dedup layer, a daily 8:30 AM sweep with AI rescheduling suggestions, and a Friday preview of the coming week.

The result. The operations team stopped reviewing calendars entirely. Violations reach Slack within 5 minutes and duplicate alerts went to zero.

Overview of the persistent vector memory MCP server for Claude Code, with architecture diagram and example recall
Infrastructure

Persistent vector memory for Claude Code

An MCP server that gives Claude long-term semantic memory: vector embeddings in Supabase pgvector, retrieved by meaning rather than keyword. Running in production daily.

The full story

The situation. AI coding assistants are stateless. Past bugs, decisions and conventions vanish when the session ends, and static notes files cannot be searched by meaning.

What was built. 8 MCP tools with token-capped recall, soft-delete memory expiry, and SQL-level filtering in a single round trip.

The result. 125 unit tests, Docker-deployed, and used in production every single day.

Voice AI · client

Voice AI receptionist for restaurant bookings

A phone agent that answers calls and takes reservations end to end. The dashboard replays every call with a transcript synced to the audio, flags interruptions, handles corrections mid-call, and produces an AI-written summary of each conversation. Built for a restaurant client. Demo above, English call.

Product · macOS

Sweep: an AI cleanup assistant for the Mac

A native macOS app that pairs a live system dashboard — RAM, disk, caches, processes — with a conversational agent. It explains what each file is and whether it is safe to remove, then cleans up on request, moving files to the Trash rather than deleting them outright. Demo above.

LLM · client

AI chat assistants for e-commerce support

Chat assistants deployed on e-commerce sites and connected to the catalogue and order systems. They cover first-line support around the clock and resolve roughly half of enquiries without a human. Each one started as a two-week paid proof of concept before going to production.

Automation · client

Counting and reporting on factory floors

Automated the counting and reporting tasks that production staff were doing by hand at the end of each shift. Each affected employee got back about four hours a week, and the reports reach management the same day instead of the following week.

Selected open source

Websites

AI video

Cinematic short films for the Bitcoin developer niche, generated end to end and published on X, where the strongest have run between 80,000 and 140,000 views.

Brand film · client

Citadel: the ecosystem debated

A 45-second brand film for Citadel: a marble senate, a dispute running across the ecosystem, closing on the client's mark. Built to hold a feed audience all the way to the logo.

140k views on X →
Vertical short

The old DAOs, cut for the phone

A vertical short set in a neon Ethereum city, weighing the old governance era against what replaced it. Subtitles are burned in and keyed to the voice track, so it reads with the sound off.

Watch on X →
Narrative short

Satoshi's ledger, told as a film

Three minutes of narrative: a lone scavenger crossing a burning world to reach the Simplicity obelisk, and the ledger underneath it. Every shot, character and voice generated, then cut and subtitled.

Technical stack

LLM & agents
Anthropic and OpenAI APIs · open-weight models (self-hosted, local inference) · agentic workflows · multi-agent orchestration · prompt engineering · RAG · MCP (published open-source server)
Evaluation
Rubric-based scoring · LLM-as-judge · frozen test sets · regression detection · threshold calibration and precision/recall trade-offs on production decision systems
Languages & data
Python · SQL · REST APIs · DuckDB · entity resolution and record linkage
Regulated data
AML / KYC screening · sanctions and PEP data (OpenSanctions, OFAC) · Companies House PSC API · MLR 2017 record-keeping · GDPR and data protection by design · audit-facing documentation
Infra
Docker · on-premise and local-first deployment · ERP / CRM / billing integration
Consulting
Discovery and operational audit · pre-sales and solution scoping · workshop facilitation · stakeholder management

Background

Nov 2025 — present

Solutions Consultant, screening & client onboardingWaverley Private Asset Partners

London multi-family office, retained around five days a month alongside Navire. UHNW client families held through trust and corporate structures. Sole technical lead on sanctions and PEP screening, reporting to the MLRO and working with the compliance function, the client onboarding team and the investment committee. Reference available from the MLRO.

Apr 2025 — present

Co-founderNavire, AI & automation consultancy

Consultancy for companies whose operations have outgrown their tools. I own discovery, pre-sales, proof of concept and production deployment end to end. Built Navire's main product, an on-premise data-privacy suite for accounting firms, now live in 25 firms.

Sep 2022 — Jan 2025

Project ManagerOnfido, identity verification platform

London. Platform processing millions of identity checks a month for 1,000+ clients across 195 countries. Ran production releases from scoping to launch across cross-functional engineering and ML teams: release planning, A/B tests, post-launch monitoring. Moved to the Fraud Lab in 2023 and coordinated evaluation and release cycles for deepfake detection models, which launched as deepfake attacks rose 31x year on year.

Sep 2020 — Aug 2022

Consultant, Data & AnalyticsWavestone

Paris. A 12-month data quality engagement at a major French bank: co-facilitated workshops with Data Officers and IT teams, produced business cases and status reports for decision committees. Then a 6-month engagement in energy, scoping the client's analytics roadmap and prioritising its use cases with data leadership.

2018 — 2019

Intern, Data & AnalyticsWavestone

Twelve-month gap-year placement in the same practice, before joining full-time in 2020.

2016 — 2020

Master in ManagementEDHEC Business School

Grande École programme, specialisation in Business Analytics, with an exchange semester at the University of Michigan, Ross School of Business.

What clients say

"I worked with Alexis for several weeks on a complex project involving the setup and integration of a CRM with our website, along with other important integrations for our business. He treated us with patience throughout the entire process, and the result is exactly what we wanted. I highly recommend him."

CRM and integrations client

"I interviewed five different software developers for a solution to automate my processes. Alexis provided a simple solution that no one else considered. Very professional and very committed to providing a successful outcome."

Process automation client

"Professional and competent as always. Responsive to last-minute inquiries and revisions throughout the year. A reliable partner and a pleasure to work with."

Repeat client, year-long engagement

Contact

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