Four capabilities recur across every Maluti venture. Together they define what we will and will not ship: intelligence that runs where the law requires, built for one domain rather than a hundred, holding a goal rather than a record — and never trusted until something deterministic has checked it.
Sovereign AI is not a marketing posture — it is an architectural constraint we accept before the first line of code. If a customer's regulator, board or client confidentiality obligation says the data cannot leave the building, then the intelligence has to come to the data.
Containerised stacks that scale from a single edge device to GPU servers. Behavioural Engine and Supervision run entirely on the customer's own hardware, on-premise or at the network edge — no cloud dependency, no data egress.
Where a product must run in isolation, it is built on open-weight models. Cost becomes compute rather than per-analysis licensing, and no vendor can change the terms, deprecate the endpoint, or see the footage.
Hosting region, inference location and identifier strategy are chosen per deployment, not inherited from a default. Where residency is not yet fully closed, we say so — it is a tracked open item, not a claim.
A general assistant with a domain prompt is not a product. Each venture is engineered around the specific physics of its problem — what the sensors can actually see, what the regulator will actually accept, what the user is actually deciding.
Current-generation language and vision models are used where they earn their place, and sized to the job. AceirMatric marks both typed answers and handwritten scans on a single unified model — one model, two modalities — at roughly $0.00004 per mark, which puts a hundred-learner pilot in single-digit dollars a month with no GPU to own.
Real environments are noisy, so we fuse. Poultry AI combines behaviour, sound, feed and water intake, environment, thermal signals and mortality before it ranks a risk. Behavioural Engine links the same physical person across overhead and eye-level cameras to answer what neither view can answer alone. Supervision runs three models in parallel — objects, scene, motion over time — and reconciles them into one score.
Where a domain has an authoritative source, that source is the ceiling. AceirMatric's questions come from roughly 972 genuine DBE past-paper PDFs and its marking is bound to the official memo. Legal AI's citations are checked against real court records. The model's job is to reason over ground truth, not to manufacture it.
Unit cost is a design input, not a post-launch surprise. Serverless inference where elasticity wins, edge processing where bandwidth is the constraint, open weights where isolation is the constraint. Every model call in Legal AI logs prompt version, tokens, latency and cost, attributable to a specific matter.
Systems of record describe the world as it is. An agent-native judgement layer sits above them and treats that world as an object to be continuously optimised — against objectives the leadership team sets, with evidence attached to every proposal.
Read-only over the existing CRM or HRIS via OAuth. No migration, no rip-out, no write-back in v1.
An event-sourced, versioned graph where activity is treated as evidence and status fields as mere claims.
Goal-holding agents audit, score, detect stalls, model futures and draft options against the stated objective.
Humans approve. Approval gates are part of the domain model, not a UI afterthought. Then the plan is exported and executed.
Because a recommendation is only defensible if you can reconstruct the world it was made in. Any past state can be rebuilt, any proposal diffed against any state, and any forecast measured for accuracy after the fact — which is how a forecast becomes something leadership can actually defend.
The deterministic-versus-LLM boundary is drawn deliberately. Momentum, coverage, span and cost mathematics are deterministic code. Language models are confined to inference and drafting — stakeholder roles, next-best actions, narrative. The number is never the model's opinion.
Every Maluti product places a verification layer between the model and the user. Nothing reaches a person ungraded, and the grader is never another model.
Whether you require the elasticity of managed infrastructure or the strict isolation of sovereign hardware, the same product is built to sit on either side of the line.
On-premise, at the edge, or air-gapped. For regulated data, surveillance footage, privileged client files, and anywhere egress is not an option.
Regional hosting with serverless or managed inference. For workloads where elasticity and unit cost matter more than physical isolation.
A scoped deployment on real data, on your infrastructure, with a defined success measure agreed before we start.
Seat- or site-based licensing of a portfolio product, with the deployment posture you require.
A bespoke venture in a domain we do not yet cover, engineered to the same doctrine and guardrails.
The useful conversation is a specific one: what decision is being made, on what data, and where that data is allowed to live.