When AI is a participant in the workflow itself, it can read, decide, draft and route — reporting what it finds and handing off to a person only where judgement is genuinely required. Here for the board-level question instead? See the executive brief. Or read our step-by-step guide on how to embed AI in your business.
At any step's entry or exit logic, Opzaro can call AI (callAI) with a defined prompt and a strict schema — the response comes back as typed, validated JSON, not free text to parse. That reliability is what lets AI output drive further workflow logic directly: a decision, a draft, or a routing outcome that becomes part of the case record, not a chat transcript someone has to interpret.
A mortgage application arrives. Opzaro extracts the key data from submitted documents, calls AI to assess risk against your lending criteria, generates a preliminary recommendation, and routes the case to the right underwriter — before anyone opens their inbox.
AI reads the submission, determines category and urgency, drafts a personalised response in your brand voice, logs it to your CRM, and routes it to the right team — all within seconds.
An invoice lands at Accounts Payable. AI validates the figures, cross-references the purchase order, flags discrepancies for human review, and auto-approves clean invoices within pre-set parameters.
A new employee joins. Opzaro orchestrates the journey — provisioning accounts, sending personalised communications, scheduling induction — with AI handling messaging and summaries throughout.
Each process gets its own fully intelligent document library — a repository of reference documents, searchable by combined keyword and semantic search. Step logic, or a person, can ask a question in plain English and get an answer drawn from that process's own material: a RAG-style assistant scoped to one business process, not general knowledge. AI calls can also reason directly over document text or images, so a step can read an uploaded invoice or ID and extract structured data automatically, with text automatically OCR'd from scanned or photographed documents.
That means AI outputs are consistent and auditable: every recommendation, draft or decision can be traced back to the documents and data that informed it, alongside the rest of the case's audit trail.
Rather than asking staff to copy information into a separate AI tool and back again, Opzaro brings the document intelligence to the step where the work already happens.
This isn't a folder with search bolted on. Every process's document library ingests, understands, indexes and cites your material automatically — the same engine covered below runs behind every "ask a question" step in Opzaro.
Contracts, spreadsheets, scanned PDFs, email threads, even zip archives — extracted and indexed automatically, with OCR for scanned or photographed pages.
The library automatically tunes how it breaks each document into passages based on its type, so retrieval stays precise whether the content is narrative legal text or dense line-item data.
Combines exact keyword matching with meaning-based semantic search, then re-scores the results for genuine relevance before the AI ever sees them.
Entities and relationships are extracted as documents arrive, forming a browsable map of how your information connects — no manual tagging required.
Real calculation, date math, unit conversion, and regex lookups for codes and IDs — plus document comparison and a hallucination check that verifies a quote actually exists before it's cited.
Each response cites the exact document and passage it came from, with a full reasoning trace available whenever you need to see exactly how the AI got there.
Every citation opens the real source file — PDF, image, or original text — with the matched passage highlighted and scrolled into view.
Metadata and knowledge-graph entities are populated automatically at ingest — no separate batch job before the library is useful.
Each process's library is fully isolated from every other tenant's, credentials are never hardcoded, and structured calculations run through a safe parser — never raw code execution.
Real value at every stage. No big-bang transformation, and no need to decide everything up front — you choose how far and how fast to go, and every AI action sits in the same audit trail as everything else.
Digitise and standardise the process as it runs today. Immediate visibility — queues, SLAs, audit trail — before AI is involved at all.
AI drafts, extracts and summarises at specific steps, with a person reviewing before anything is finalised. Low risk, immediate time savings.
Within parameters you set, AI auto-approves, auto-categorises or auto-routes — escalating to a person only for exceptions.
AI participates across multiple steps, coordinating handoffs between systems and people — with full audit trail and human oversight on exceptions throughout.
An AI strategy that the board can sign off on needs answers to the same questions as any other operational change: what can it do, who's accountable, and what happens when it gets something wrong.
AI actions sit in the same audit trail as human actions — who, what, when, and on what basis.
Token cost from AI calls is tracked and accumulated against each task, so you can see and report on AI spend by process or by case.
Each step's permissions, SLAs and exit conditions apply to AI just as they do to people — AI works inside the guardrails you set.
Processes, configurations and data can be exported, shared and evolved as your business changes — not locked into a single vendor's roadmap.
Start with the process as it runs today, digitised as a real workflow with queues, SLAs and an audit trail — before AI is involved at all. Add AI assistance at one or two specific steps next, with a person reviewing its output before anything is finalised. That's a working result in weeks, not a six-month AI strategy project.
Yes, when AI acts inside guardrails you define. Every AI action sits in the same audit trail as a person's, and each step's own permissions, SLAs and exit conditions apply to AI exactly as they do to staff.
Cost is visible per task, not per subscription: token cost from AI calls is tracked and accumulated against each individual task, so you can report on AI spend by process or by case.
A chatbot answers questions beside your process; embedded AI is a participant inside it — returning typed, validated data that can directly drive a decision, a draft or a routing outcome as part of the case record itself.
We'll show AI acting inside a real process — not a demo chatbot — using an example close to your operation.