Solutions · five practices
Algobain designs, builds and operates AI agents, AI-native products, data and ML platforms, the cloud beneath them, and the legacy systems they have to work with. Each practice stands alone. Together they are the system your AI runs on.
Every engagement starts the same wayA two-week, fixed-fee architecture assessment, whichever practice leads.
01 How they fit
An agent is only as reliable as the data it reads, the platform it runs on and the systems it acts on. We build each layer to carry the one above it, and we stay on to run the whole thing.
Reading Fig. 1
Read it from the base up. Legacy systems are opened at the seams so they can carry new work. The cloud platform is provisioned as code and observed from the first deploy. The data platform feeds models that can be retrained and explained. Agents act on top of all of it, and every consequential action waits at a gate a named person owns. The product surface is where your people and customers meet the result.
02 The five practices
Each practice is measured against an outcome, not a deliverable list. The figures below come from systems Algobain built; anything not yet verified is shown as a placeholder.
Agents that read context, make calls and act inside your systems: support and triage, research and reporting, workflow automation and multi-agent orchestration. Every consequential action passes an approval gate a named person owns, and every decision is logged.
Web and mobile products designed around a model from day one, not a chatbot bolted onto an existing interface: semantic search, intelligent dashboards, recommendation and matching, mobile AI interfaces. Built to production, measured by outcome.
Pipelines, warehouses, feature stores and scoring services that turn raw data into decisions you can act on, in real time, with the lineage to trust them and the monitoring to know when a model drifts.
The platform beneath the models: provisioned as code, observable by default, built for low latency and high availability, and sized for a bill you can defend. Availability, observability and cost are visible from the first deploy.
Monoliths taken apart incrementally with the strangler-fig pattern: behaviour pinned with characterization tests, capabilities moved one at a time, and AI added at the seams. No big-bang rewrite, no feature freeze.
03 How an engagement runs
You decide with a document in hand, and you can stop at any step. The team that writes the assessment is the team that builds and runs the system.
Engagement path
Fixed-fee architecture assessment. Roadmap, ROI, build-versus-buy.
Target design, data contracts, governance and the approval-gate map.
Dedicated senior delivery team, minimum three months, shipping to production in increments.
Managed operations: monitoring, retraining, incident response, cost control.
Before the work starts
04 The numbers
Operating cost automated across client systems.
Cumulative, all engagementsUptime on the production AI systems we operate.
Managed operationsTime to a reply from a senior engineer, not a sales desk.
Every inbound requestFixed-fee architecture assessment before any build commitment.
Every engagement
Most engagements touch more than one. Tell us what you are trying to run in production and the assessment will say where to start.