Built AI · Service
AI Automation. Remove the manual layer.
Answer
Answer: Built AI designs and ships internal tools and end-to-end automations that combine deterministic workflows with AI where it matters. The result is operational leverage — manual work eliminated, cycle times collapsed, and teams freed to focus on what humans should actually be doing.
What we build
Internal back-office tools, AI-native dashboards, document and email pipelines, integration layers between SaaS systems, and data workflows that turn raw inputs into decision-ready outputs. Every engagement is owned end-to-end: discovery, architecture, engineering, deployment and the operational rigour required to keep the system reliable in production.
Document workflows
Extract, classify, route and validate documents at scale across operations, finance and legal.
Operational tools
Custom internal apps that fit the team's actual workflow, not the off-the-shelf compromise.
System integrations
Glue between CRMs, ERPs, mailboxes, data warehouses and AI — with auditability and idempotency built in.
Sales operations
Lead enrichment, routing, scoring, drafted follow-ups and CRM hygiene that runs without analyst time.
Finance & RevOps
Reconciliation, anomaly detection, scheduled reporting and approvals that no longer need a spreadsheet.
Support & ops
Triage, summarisation, knowledge retrieval and escalation paths that compress response times.
Automation vs. AI agents — when to use which
Most production systems use both. Deterministic automation is the right tool when the workflow is rule-bound and the inputs are structured; AI agents earn their seat when judgement is needed over unstructured inputs. We design every engagement to put each step in the right layer.
| Dimension | Deterministic automation | AI agent |
|---|---|---|
| Inputs | Structured | Structured + unstructured |
| Cost per run | Near zero | Higher (model + tools) |
| Latency | Milliseconds | Seconds |
| Failure mode | Predictable | Probabilistic |
| Best for | High-volume, rule-bound | Judgement, ambiguity |
How we engage
Engagements typically run in three phases. Discovery (1 week): map the workflow, the data and the success metric. Build (3–8 weeks): architecture, integrations, AI components where appropriate, evaluation harness, deployment. Run (ongoing, optional): observability, iteration, drift checks and incident response. Every deliverable is owned by the client.
Measurable outcomes
We agree the metric before we start — hours saved, errors prevented, cycle-time reduction, throughput per FTE — and instrument the system so the numbers are visible in real time. Automation that can't be measured shouldn't be shipped.
Workflow examples we ship most often
Document operations. Inbound contracts, invoices, KYC packs and onboarding documents are extracted, classified, validated against business rules and routed into the right system of record. Exceptions are summarised and queued for human review with the relevant context attached. Typical impact: cycle-time reduction of 60–80% and headcount redirected to judgement work.
Sales and revenue operations. Lead capture is enriched against external data, scored against ICP, routed to the right owner with a drafted first-touch message, and held in the CRM with the data hygiene rules running on a schedule. The analyst time previously spent on list-building and CRM cleanup disappears.
Finance and reporting. Reconciliations between payment processors, ERP and the data warehouse run on a cadence with anomaly detection on top. Scheduled reporting compiles board, investor and management packs from live data, with commentary drafted by an AI step and approved by the controller.
Support and operations. Inbound tickets are triaged, summarised against history, matched to known resolutions and either auto-resolved within scope or escalated with full context. Response times collapse; agents handle the cases that need humans.
Implementation phases in detail
Discovery (1 week). We map the end-to-end workflow, the data sources, the integration surface and the success metric. The output is a one-page architecture sketch and a scoped statement of work — not a slide deck.
Build (3–8 weeks). Engineering happens in two-week increments against a working environment. Integrations, deterministic logic and any AI components are built in the same codebase, behind clean interfaces, with evaluation harnesses for anything probabilistic. Code is owned by the client from day one and lives in their infrastructure.
Deploy and instrument. Production rollout is staged behind feature flags, with observability covering system health, throughput and — where AI is in the loop — model behaviour and cost. Runbooks and on-call playbooks ship with the system, not after it.
Run (optional, ongoing). Many clients keep us on for steady-state operation: drift checks, eval refreshes, model and provider swaps, incident response and continuous iteration. Others take the system fully in-house once it has bedded in.
Measurable business outcomes
We commit to the metric before we start. Across a typical twelve-month engagement, the systems we ship deliver 30–70% cycle time reduction on the targeted workflow, error rates cut by half or better, and analyst capacity redirected to work that compounds instead of work that maintains. The systems pay for themselves on hard cost in the first two quarters and on opportunity cost from then on.
FAQ
Do you replace platforms like Zapier or Make?+
Sometimes. For simple, well-bounded flows, off-the-shelf no-code is fine. We come in when integrations need AI in the loop, custom logic, regulated data, or scale beyond no-code limits — typically once a workflow becomes business-critical.
Who owns the code?+
You do. We deliver fully owned codebases on your infrastructure, with documentation, evaluations and runbooks. There is no lock-in to a Raise AI Ltd platform for automation engagements.
How long does a typical automation project take?+
Most production automations ship in 4 to 10 weeks. Internal tools and dashboards generally land at the lower end; multi-system integrations with AI in the loop at the higher end. We scope precisely after a discovery week.
How is success measured?+
Per workflow, before we start. Common metrics: hours saved per week, error rate reduction, cycle-time reduction, throughput per FTE. We instrument the system so the numbers are observable, not anecdotal.
How does this differ from AI agents?+
Automation runs deterministic rules; agents reason at runtime. Many systems use both — automation as the rails, agents on the edges where judgement is needed. See our primer on agents vs. automation.