ADAPTIVE DATA RISK MANAGEMENT
Infolock came to maiven to build it, under one hard rule. Sensitive data never leaves the customer’s environment. Six months later, the first decisioning layer for data risk in the era of AI was running in production. Fixed price, fixed scope, zero change requests.
6
months from project start to production handover
0
change requests across the build
7
production layers, from streaming ingestion to full observability
Tens of thousands
incidents inspected daily by AI
In data risk, a system that almost works is worse than nothing.
More of your data flows through AI systems every month. The risk of it moving somewhere it shouldn’t grew right along with it. And most tooling still logs and alerts after the fact instead of deciding what matters in the moment.
The demo catches the obvious case. The prototype impresses the board. Then it meets real traffic and the actual work starts. Ingestion that holds under load, triage an auditor can follow, models that don’t ship your data to someone else’s cloud, observability, uptime. Most AI tooling stalls right there. A system that only half works hands you false confidence, which is its own kind of risk.
[TIMELINE]
Idea to production. No surprises.
OCT 2025. FIRST CONVERSATION.
Infolock needed an adaptive data risk system under a non-negotiable constraint.
The model and the data stay inside their boundary. The question on the table was whether it could be built production-grade on a fixed timeline.
NOV 2025. A MONTH OF DISCOVERY.
A structured discovery process.
Scope defined, architecture mapped, every requirement captured and validated. Fixed timeline and fixed investment agreed before a line of code was written. No scope creep, no cost-plus surprises.
JAN 2026. BUILD STARTS.
With everything fixed up front, the usual client-vendor boundary came down.
maiven and Infolock built as one team, every day.
APR 2026. ALPHA.
The full system running end to end for the first time.
Signal to recommended action.
MAY 2026. BETA.
Hardening, real-data runs.
The system becoming something a risk team can actually operate.
JUN 2026. UAT AND PENETRATION TESTING.
User acceptance testing alongside independent penetration testing.
A system that guards data has to survive being attacked before it ships. It did.
30 JUN 2026. PRODUCTION HANDOVER, AS PLANNED.
Six months, greenfield to production.
Zero change requests across the build.
[WHAT MAIVEN BUILT]
Adaptive Data Risk Management, in production. Not a pilot.
The constraint shaped everything. The system below is what a decisioning layer for data risk looks like when it has to run in production and keep every signal in-house.
01
Streaming ingestion that pulls every kind of security signal into one normalized shape, built on a unified data model of 350+ fields so nothing downstream has to care where a signal came from.
02
A triage engine that classifies and ranks incidents, rules and ML together.
03
Enrichment services that gather the context an analyst would otherwise chase manually.
04
Agentic workflows that carry a case from raw signal to recommended actions.
05
A case management layer where a human reviews every case and makes the call.
06
Self-hosted LLM deployment offered as an option by design, so sensitive data can stay inside the customer’s environment and run on their own infrastructure.
07
Security and observability under all of it, including the AI inference itself, with the whole environment defined in code so it can be rebuilt and audited.
None of that is the glamorous part. All of it is the difference between a system that runs in production and a demo that dies in a pilot.
“The thing I keep coming back to is how little friction there was. We set the scope up front and it held. When something came up, the answer came back fast and straight. The constraints were hard, and they treated that as the starting point instead of arguing it. A complicated build, and it felt under control the whole way.”
Matt Reid
VP Emerging Technologies, Infolock
[WHAT THIS MEANS FOR YOU]
Infolock walked a path you can walk too.
If you have an AI product idea, a stalled pilot, or a compliance constraint that rules out the usual cloud AI tooling, it’s the same path. The timeline is fixed and the investment is fixed, and at the end you own all of it, code, infrastructure, and IP. If the data has to stay inside your walls, it can.
Infolock is the first of these. A system built from the ground up, to a hard data-residency constraint. The same delivery discipline runs through all three.
Three ways to put AI into production.
Launch a new AI product you own.
An end-to-end build. A net-new agentic AI product that opens a new revenue line or anchors a new business. We take it from concept to production with a dedicated squad, on a fixed timeline and a fixed investment, production-grade from day one. You own the product, the code, and the IP.
From €250,000 · from 16 weeks
Make the software you already sell win more deals.
We build production-grade AI straight into your existing product. No replatform, no stalled pilot. New AI modules you can sell, and features that defend your pricing and win deals. First capability live in months, owned by you.
From €100,000 · from 12 weeks
Install AI pointed at the numbers you own.
For operating companies, not software vendors. We install agentic workflows on your data, in your environment. A growth side that researches your market and your accounts and produces the output that wins work. An operations side that takes high-volume work off your most expensive people. Live in 8 to 12 weeks, owned by you.
From €75,000 · from 8 weeks
[MORE PROOF]
