AI Desk

AI Business Automation Platform

Industry: Cross-industry Back Office
Duration: 4 months
Delivered: 2025
Client: AI Desk (demo)

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AI Desk automates routine back-office work by combining document understanding, retrieval and a workflow engine behind a single console. Incoming requests arrive by chat, email or upload, are classified by intent, and are either handled automatically or routed to a person with the supporting context already assembled. A retrieval pipeline grounds answers in the organisation's own documents, while guardrails and evaluation checks constrain what the system is allowed to decide. As a demo, it illustrates where automation is reliable and where human approval should remain in the loop.

The challenge

What the business was up against

Back-office teams were drowning in routine requests that arrived through every channel at once. Documents were read and re-keyed by hand, the same questions were answered repeatedly, and knowledge sat in shared drives that nobody could search effectively. Work queued in inboxes with no shared view of priority or ownership, so urgent items waited behind trivial ones. Managers could not tell how much effort went into each request type or where the real bottlenecks were. Every automation attempt stalled because there was no audit trail and no safe way to keep a person in the loop for sensitive decisions.

At a glance

Industry: Cross-industry Back Office
Duration: 4 months
Delivered: 2025
Engagement: Discovery → delivery → support

The solution

What we designed and shipped

We built a single intake and orchestration layer in front of the existing systems. Requests arriving by chat, email or upload are classified, deduplicated and routed by intent, with a retrieval pipeline that grounds every response in approved source documents. Routine cases are handled end to end by the workflow engine, while sensitive or low-confidence cases are escalated with the evidence already assembled, so a person only makes the judgement call. Guardrails and evaluation checks run before any action is committed, and every decision, suggestion and override is written to an audit log that operations can review.

At a glance

Primary category: AI
Services: AI · Enterprise · Web
Team: Product, design, engineering, QA
Delivery: Two-week increments

Key features

What the platform actually does

AI Assistant

Conversational surface grounded in approved organisational knowledge and prior resolved cases.

Document Processing

Extraction and classification of structured fields from uploaded files.

Workflow Automation

Rule and model-driven routing of requests through to completion.

Knowledge Search

Retrieval over internal documents with cited source passages.

Recommendation Engine

Suggested next actions ranked by confidence and policy fit.

Analytics & Oversight

Automation rates, accuracy and escalation trends by request queue.

Architecture

How the system is put together

Each tier can be scaled, replaced or taken offline independently. Data flows left to right; failure in a downstream tier never blocks the primary transaction path.

Interfaces

Every intake channel funnels into one classified request queue.

Web consoleOperators review, approve and override automated decisions.
Chat surfaceConversational intake with context carried between turns.
Email intakeInbound mail parsed and matched to existing requests.

AI orchestration

Routing, retrieval and safety checks wrapped around model calls.

Intent routingClassifies each request and selects the handling path.
RAG pipelineRetrieves approved passages and grounds generated responses.
Guardrails & evalsPolicy checks and scored evaluations before actions commit.

Automation

Execution layer that connects decisions to real systems.

Workflow engineRuns steps, retries failures and records state transitions.
IntegrationsConnectors to mail, storage and internal business systems.
Human approvalEscalates low-confidence or sensitive cases with evidence attached.

Data

Operational records, semantic index and decision history.

PostgreSQLRequests, workflow state and operator decisions.
Vector indexEmbeddings for semantic retrieval across approved documents.
Audit logImmutable record of automated and human decisions.

Technology stack

Chosen for the decade, not the demo

Every dependency here has a long support horizon, an active community and a large hiring pool. That keeps total cost of ownership predictable long after launch.

Demo project

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