Trust · Proof library
For premium B2B engagements, marketing copy isn't enough. This is a library of the concrete artifacts we ship — the maps, dashboards, audits, reports, and architectures you can actually inspect before and during an engagement.
Representative samples. The artifacts below illustrate structure and depth. Client-specific versions are produced during the engagement and shared under NDA.
Engineering proof
Profitec AI builds AI systems where data pipelines, business rules, specialist agents, risk gates, human review, and monitoring work together inside one auditable workflow.
That discipline comes from projects where a recommendation cannot be treated as a casual AI output: the system has to validate inputs, measure confidence, check risk rules, log decisions, and route uncertain cases back to a human.
Sources connected, validated, and normalized before any model runs — so AI acts on data it can trust.
Data pipelinesNarrow agents that classify, summarize, review, draft, and route — each with structured inputs and clear outputs.
AI agentsA control layer that decides whether a workflow continues, pauses, escalates, or falls back to a human.
Security & controlsSensitive and judgment-heavy steps route to a person before any external action is taken.
Approval gatesEvery decision is visible: status, audit logs, error rate, and exactly what the system did — and did not — do.
Reporting systemsA working multi-agent decision system with data validation, an ML prior, a 7-check risk gate, and refusal logic.
Market Intelligence CrewSample deliverables
01
End-to-end maps of how a process moves before and after automation — every trigger, decision, system write, and human gate drawn explicitly.
02
Operational dashboards we ship with engagements — citation rate, pipeline movement, throughput, and exception monitoring in one view.
03
What an AI visibility or automation audit actually delivers: a baseline, a structural delta against competitors, and a ranked, do-this-first roadmap.
04
The monthly report a client receives — per-prompt, per-engine breakdown with the source pages doing the work and any factual-error flags.
05
A reference architecture for capturing inbound into a CRM process — intake, enrichment, routing, and follow-up — with controls drawn in.
06
Side-by-side of a manual workflow versus the automated target — same process, fewer manual steps, measurable checkpoints.
07
A realistic week-by-week plan from discovery to first results — with the shipped artifact expected at each phase.
Documented engagements
AI citation rate 16.1% → 62% in 8 weeks. Full teardown of audit, schema, and bilingual strategy.
Read the teardownOperations layer for vehicle insurance claims — extraction, validation, routing, and human review gates.
Read the teardownMulti-agent orchestration with data validation, risk gates, and refusal logic — a technical systems example.
Read the teardownHow these systems are kept safe is documented on the Security and Controls page.
Tell us the workflow or the AI-search category you care about, and we'll walk through the relevant artifact — a real audit, dashboard, or architecture — on a short call.