AI Workflow Orchestration Service
We design and implement governed AI agents and agentic workflow systems connecting tools, inboxes, data, and teams into repeatable delivery paths with control points, approval gates, and evidence trails.
These systems can be applied in three delivery contexts: - Organisation workflows, where operational processes are improved across functions such as service delivery, reporting, knowledge management, and coordination; - Programme/project workflows, where delivery infrastructure is built into a project, consortium, or funded programme; - Single capability assets (Pilots), where one specific governed system is delivered, such as intake triage, a compliance-pack builder, or a reporting pipeline.
In brief: Workflow orchestration is the control layer that routes work across people and systems, invokes bounded AI when useful, and enforces checks, approvals, logging, and exception handling so delivery is repeatable and ownership stays clear.
Three Packages
Know where to start. Build what you need.
From assessment to full system. Each package is a discrete, buyable engagement—not a consulting retainer with no defined output.
Entry point
PACKAGE 01
AI Workflow Opportunity Sprint
For organisations that know they need AI automation but aren't sure what to build first.
Best for
SMEs, NGOs, consultants, public bodies, and research teams with too many manual processes, repeated drafting tasks, fragmented tools, or slow coordination.
What's included
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Review of your current tools, inboxes, knowledge flows, and recurring tasks
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Identification of the 3–5 highest-leverage automation opportunities
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Risk and governance assessment: what can be automated, what needs approval, what shouldn't be automated
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Recommended first workflow or pilot
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Practical implementation roadmap
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Summary document with priorities, effort level, expected value, and next steps
Client Outcome
You know exactly where AI automation creates the most value and what to build first.
Most Popular
PACKAGE 02
Governed AI Workflow Pilot
For organisations ready to test one working AI-enabled process under real conditions.
Example Pilots
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Email triage and drafted replies with approval
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Research brief pipeline
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Proposal or bid drafting workflow
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Reporting pack builder
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Internal knowledge assistant
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Lead capture and qualification workflow
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Controlled content production workflow
What's included
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Process design and workflow mapping
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AI role definition: what AI drafts, classifies, retrieves, checks, or routes
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Automation build using appropriate tools and integrations
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Human approval gates for sensitive outputs
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Basic logging and traceability
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Testing with real or realistic cases
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Runbook and handover documentation
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Recommendations for scaling
Client Outcome
A working minimum AI-enabled workflow that proves value while keeping human oversight and operational control.
Full System
PACKAGE 03
AI Delivery System Build
For organisations that need a complete operational system, not a small test.
Best for
Clients with recurring operational, reporting, proposal, research, communication, or coordination processes that need to run reliably across people, tools, and documents.
What's included
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End-to-end workflow architecture
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Integration of tools, documents, data sources, forms, inboxes, and project systems
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AI functions for drafting, classification, extraction, synthesis, or retrieval
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Role-based review and approval process
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Error handling and escalation logic
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System of record for decisions and outputs
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Documentation, handover, and operating model
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Governance controls and scale plan
Client Outcome
A governed AI-enabled delivery system that improves speed, consistency, traceability, and operational reliability.
Benefits to the client
Typical outcomes include:
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Faster execution and stronger decision support (with accountability preserved)
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Higher consistency across communications, documents, and reporting
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Better control of organisational knowledge (less loss across tools/people)
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New operational capabilities (not only replacing repetitive steps)
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Auditability by default: approvals, logs, role-based routing, and traceable decisions
Governed Workflow Architecture
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Orchestration logic: modular routing, webhooks, list processing, and case handling (maintainable visual workflows)
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State and resilience: persistent records, validation rules, retries, error routes, and alerts
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Bounded AI functions: extraction, classification, drafting, consistency checks, structured outputs
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Controls and governance: approval gates, permissions, role-based routing, escalation rules, audit logs
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Data governance: source boundaries, privacy handling, and documentation of assumptions and limitations
What This Looks Like in Practice
These examples show common first deployments selected for leverage, risk profile, and measurability.
Client Inquiry & Service Agent (Email / Teams / Slack / Telegram)
Drafts high-quality replies grounded in approved internal knowledge, routes messages to the correct owner, and holds sensitive outputs behind an approval gate before sending. All interactions are logged to a system of record, with follow-ups triggered automatically.
Primary gains: faster response time, higher consistency, continuity across staff changes.
Proposal / Bid / Concept Drafting Pipeline
Converts internal inputs and partner contributions into structured documents — concept notes, bid drafts, programme narratives — collected via forms, consolidated, and prepared for review cycles. Gaps and conflicts are flagged before integration, not after.
Primary gains: faster drafting cycles, fewer missing elements, less rework.
Lead Capture, Enrichment & Qualification
Captures inbound leads from forms, email, and messaging automatically, enriches and scores them, and routes each to the right owner with a single interaction history. Reminders and next steps are triggered without manual follow-up.
Primary gains: faster conversion cycles, reduced leakage, measurable pipeline.
Research & Intelligence Operations (web + internal knowledge)
Monitors selected sources, produces structured briefs, and writes findings into a shared database for reuse across teams. Agents can generate decision packs — what changed, why it matters, recommended actions — and route them to the right owner.
Primary gains: faster evidence-building, reduced research overhead, stronger decision support.
Reporting & Compliance Automation
Collects periodic inputs, validates completeness, and produces review-ready reporting packs with evidence registers and drafted narrative sections. Exceptions are escalated; audit trails record who approved what and when.
Primary gains: fewer reporting failures, predictable cycles, higher traceability.
Operational Control Tower (cross-tool execution)
A coordination layer where inbound inputs automatically update structured records, create tasks, refresh dashboards, and notify the right people — with routers handling different cases and error routes managing failures without breaking the operation.
Primary gains: less tool fragmentation, better visibility, fewer handoff failures.
Built from Proven AI Tools, Governed for Real Operations
Post AI Systems focuses on deploying proven, production-ready AI products (off-the-shelf) and platforms (rather than building bespoke models from scratch) and wiring them together into governed end-to-end workflows. That approach shortens time-to-value and reduces long-term maintenance risk, because components are standardised, well-supported, and easier to replace or upgrade. Where it makes operational sense, we implement these systems on enterprise low-code/automation platforms so teams can monitor, adapt, and extend the workflows without heavy custom engineering—while keeping clear controls, audit trails, and accountable ownership in place.
FAQ: AI Workflow Orchestration
Q1: What operational artefacts are produced during an orchestration build (beyond “a workflow”)? An orchestration build produces: a process map with decision points, routing logic with exception handling, a state model (what is recorded and where), validation rules, approval gates, and an evidence trail configuration that links inputs → drafts/actions → approvals → final outcomes. Q2: Which systems can be connected, and what is treated as a “system of record”? Common connections include email, documents, databases, CRMs, project tools, forms, and internal repositories. A system of record is defined for each workflow so decisions, approvals, and key outputs are written back to an authoritative store with version history. Q3: How is reliability handled when inputs are messy (threads, attachments, partial data, edge cases)? Reliability is handled through: structured intake parsing, validation and completeness checks, persistent state, retries, error routes, escalation rules for ambiguous cases, and fallbacks that route uncertain outputs to human review rather than auto-execution. Q4: Where are bounded AI components permitted inside an orchestrated workflow? Bounded AI components are used for constrained tasks such as extraction into structured fields, classification, drafting variants for review, consistency checks, and retrieval-grounded summaries. Publishing, sending, committing records, or producing high-impact outputs remains behind explicit approval gates. Q5: What evidence is captured to support auditability and post-hoc review? Auditability is supported by: timestamped logs of key actions, role-based routing records, stored inputs and intermediate artefacts, approval records, and traceability links that connect each output to its sources and reviewers.
