Airclerk puts AI to work on the engagement load around client delivery at IT consultancies, systems integrators, management consultants and agencies: the proposal, the statement of work, the deliverable checked against it, the status report and the close-out. The engagement lead keeps the calls on scope, quality and the client.
We build around the systems, templates and precedent the firm already runs on. Airclerk is run by people who ran an implementation consultancy of their own: Dan, Matt and Ed shipped more than one hundred projects together at Trineo, one of New Zealand's most successful cloud-platform implementation partners, before Airclerk existed. The delivery shape will be familiar even where the discipline differs.
We implement Claude, Microsoft Copilot and ChatGPT; the platform is chosen around your systems and the work.
A draft deliverable arrives the night before the milestone review. The workflow reads it against the SOW and the firm's quality checklist, and hands the engagement lead a marked-up view: what is covered, what is thin, what is missing, and what was written that nobody asked for.
Illustrative example
The engagement lead uses the flagged gaps to focus the review, and checks the draft before signing off, and makes the three decisions that are theirs to make: send D2 back for costing, get D3 written, and either raise a change order for the migration work or cut it. The workflow decides none of that. It writes the check up as an AI Workpaper, with the sources it read and the gaps it raised, so the file shows the review happened before the milestone was signed.
The commercial point is in rows D2 and X1. Thin deliverables become disputes about whether the work matched the brief. Work outside the SOW becomes a write-off nobody agreed to. Both are cheaper to catch the night before the review than after the invoice.
Deliverable QA is one of ten shapes we see repeat across consultancies and agencies. Each is a candidate first workflow, adapted to the firm's delivery method, systems, templates and precedent. All of them are illustrative until they are built on your engagements.
Turns the client brief, discovery notes, prior engagements and the firm's commercial templates into a first-draft proposal and statement of work. Scope, deliverables, assumptions, exclusions, dependencies and pricing structure are separated clearly for principal or director review.
Assembles a draft RFP response from the tender document, the firm's approved boilerplate, past winning responses and relevant case examples. Requirements are traced through to the response so gaps against the brief are flagged before the bid lead reviews it.
Checks a draft deliverable against the statement of work, the client's brief and the firm's own quality checklist. Flags gaps, unaddressed requirements and departures from what was agreed, so the engagement lead reviews a marked-up draft rather than starting the check from a blank page.
Classifies incoming client and engagement correspondence by project, urgency and required action. Drafts responses for routine items, flags anything touching scope or price, and updates the open-issues register. Nothing is sent without the engagement lead's approval.
Turns raw time entries and task notes into clear, client-ready narrative for invoicing and internal utilisation reporting. The underlying hours and rates are not altered by the workflow; it drafts the description, not the number.
Builds the regular client and internal status report from task tracking, correspondence, risk logs and commercial data. Consistent structure, current risk items and next steps are ready for the engagement lead to review before it goes to the client.
Assembles the final deliverable index, outstanding-item register, decision history and handover material at engagement close. Drafts a lessons-learned summary from project correspondence and retrospective notes for the delivery lead to confirm.
Reviews a draft MSA, SOW or change order against the firm's standard terms and flags clauses that depart from them - liability caps, IP ownership, payment terms, termination rights. Surfaces the departure for a partner or legal reviewer; it does not negotiate or approve terms.
Searches past proposals, SOWs, deliverables and playbooks the firm has approved for reuse, and that the person running it may access, for a relevant precedent when a new engagement or bid comes in. Surfaces the source engagement and document rather than generating a plausible-sounding answer from nothing, so the firm's own delivery history stays usable after the person who ran it moves on.
Drafts a staffing recommendation for an upcoming engagement from skills data, availability, prior client feedback and utilisation targets. The resourcing decision and any client-facing commitment about who is staffed remain with the practice or delivery lead.
Optional AI Process Assurance can sit behind the workflows we implement: the instructions, sources, exceptions, review changes and sign-off retained as an AI Workpaper, so the firm can show what the AI did, what it did not do, how the result was checked and who approved it. That is useful evidence the day a client disputes whether a deliverable matched the brief. It is a platform subscription, implemented alongside the workflow, and the workflow runs without it.
Airclerk can implement Claude, Microsoft Copilot or ChatGPT depending on the firm's existing environment. Workflows run on the firm's approved enterprise instance and under its own vendor agreement.
Where AI could make the biggest difference, how that work runs today, the systems it touches and who owns it. We also work out together whether Airclerk is the right partner for it. If we are, the conversation gives us enough to quote.
One document: the outcome we are aiming for, what we deliver, what your team contributes, the fixed fee and timeline, and what acceptance looks like. Nothing starts until it is agreed on both sides.
The engagement opens with the detailed design, agreed with you before anything is built. Then we configure and connect the workflow to your documents and systems, build in the agreed review controls and audit trail, test it on real work, and prepare the people who will run it.
Handover, and the support that follows go-live, are written into the scope so you know what is covered before you sign. Ongoing operations and further workflows are a separate agreement, made when you want them rather than switched on by default.
Read how an engagement runs in detail →
The AI can check a draft against the statement of work and flag gaps, but the judgement call - is this actually what the client asked for, is it good enough to ship - stays with the engagement lead. That call is often central to a scope dispute or PI claim, and it should be made by an accountable person, not inferred from a model's confidence.
A workflow can assemble the evidence that a milestone is ready - deliverables, review notes, outstanding items - but the sign-off itself, and the record that it happened, stays a deliberate human action with a name attached to it.
Invoicing narrative, contract terms and pricing changes can be drafted by the workflow, but they are approved and sent by a person. The workflow does not have authority to commit the firm to a price, a term or a deadline.
The objective is not to make the AI the consultant. It is to make the engagement file more complete before a person reviews it: the brief traced through to the deliverable, correspondence triaged, precedent surfaced, status reporting current and the review trail intact. That is where speed and defensibility can reinforce one another.
The first workflow is chosen by volume, source availability, repeatability, review effort and contractual risk. For many firms that means proposal drafting, RFP response assembly, deliverable QA against the brief, status reporting or engagement close-out. Scope determinations, milestone sign-off, contract negotiation and client-facing commitments remain outside the first workflow.
Each workflow defines who reviews what: engagement lead, practice lead, delivery director or another authorised reviewer. The workflow stops at the appropriate boundary. It does not send client correspondence, approve a change order, close a milestone or issue an invoice by itself. The AI Workpaper records which sources were used, what exceptions were raised, what changed during review and whether any required gate was missed.
The workflow is evaluated against held-out examples from the firm's own completed engagements. Does it trace the brief correctly? Does it distinguish evidence from assumption? Does it identify missing information instead of inventing it? Does it maintain client and engagement isolation? Does it route scope questions to the right person? Cross-client leakage, prompt injection, unsupported claims and false-completeness checks form part of the production gate.
The engagement produces: the configured workflow on the firm's approved AI platform and systems; a Workflow Charter defining its stages, permitted sources and approval gates; a clear boundary statement describing what the workflow must never decide or do; evaluation results from held-out engagement testing; a client and engagement isolation model; a runbook for engagement leads, delivery staff and support teams; training appropriate to each role; a retained AI Workpaper for each material run; the check-ins after go-live that confirm the workflow is being used safely, shaped to the firm and written into the scope. Ongoing operations beyond that are a separate agreement.
IT consultancies, systems integrators, management consultants and agencies use AI models such as Claude, Microsoft Copilot or ChatGPT to support the engagement work behind client delivery: proposal and SOW drafting, RFP response assembly, deliverable QA against the brief, engagement correspondence triage, timesheet and utilisation narrative support, project status reporting, engagement close-out and lessons-learned capture, contract and MSA review support, and knowledge search across past engagements. The assistant can be connected to the firm's practice-management system, document store, email, CRM and approved templates. Client and engagement isolation, source citations and engagement-lead approval are built into the workflow. The AI supports the work around delivery judgement. It does not decide whether a deliverable meets the statement of work, sign off a milestone or negotiate on the firm's behalf.
IT consultancies, systems integrators, management consultants and agencies can use AI to support the document and correspondence work around client engagements: proposal and SOW drafting, RFP response assembly, deliverable QA against the brief, engagement correspondence triage, timesheet and utilisation narrative support, project status reporting, engagement close-out and lessons-learned capture, contract and MSA review support, and knowledge search across past engagements. Airclerk implements these as governed workflows connected to the firm's approved practice-management, document and knowledge systems. Engagement leads keep the sign-off on scope, quality and client communication.
Not in the workflows Airclerk recommends as a starting point. The AI can draft and check, but the firm's engagement lead decides whether a deliverable meets the statement of work, whether a milestone is ready for client sign-off and how a scope question gets resolved. Those calls carry contractual and professional-indemnity weight, and they stay with the person accountable for the engagement, not with the model.
Two controls come first. Client and engagement isolation: material from one client's engagement is walled off from another's, including where two clients compete with each other. Permission-aware access: the workflow sees only the engagement material the person running it is already authorised to access in the firm's existing systems. Before engagement information is connected, Airclerk reviews the AI vendor's enterprise terms, retention, training use, subprocessors and data-location arrangements. The implementation runs on the firm's approved enterprise instance, under its own agreement with the AI provider.
AI Process Assurance records the work that passes through an instrumented workflow. A Workflow Charter defines the expected process, approved sources and the review gates a deliverable must pass before it goes to the client. A Semantic Audit records what actually happened during the run. The retained AI Workpaper shows the sources used, exceptions raised, review changes and final sign-off - useful evidence if a client later disputes whether a deliverable matched the brief. It does not claim to capture every conversation a consultant has with a client. It creates a reviewable record for the specific AI-assisted workflows the firm chooses to govern.
Airclerk works with IT and technology consultancies, systems integrators, management consultants, and creative and marketing agencies - firms that sell expertise and bill for engagements rather than a regulated financial product. The first workflow is chosen around the firm's actual delivery model rather than its label. A boutique agency and a multi-practice systems integrator may share the same high-value starting point, such as proposal drafting or deliverable QA against the brief.
We are not aware of one, and we would be cautious of one. Delivery methods, contract structures, tooling and client-confidentiality obligations vary too much between firms for a generic package to be installed safely. Airclerk brings the implementation method, governance controls, workflow architecture and reusable patterns. The firm brings its engagement leads, systems, templates, precedent library and delivery standards. The first engagement starts with a first conversation and runs as a co-design process, not a generic consulting brain delivered from outside the practice.
AI features inside PSA, practice-management and productivity platforms are useful, and Airclerk does not try to replace them. The workflow layer crosses systems and follows the firm's process: it can begin with the client brief, retrieve the correct engagement documents, apply the firm's approved checklist, prepare a draft, route it to the engagement lead and retain the evidence of review. The firm controls the configured process, boundaries and approval gates. We implement Claude, Microsoft Copilot and ChatGPT; the platform is chosen around your systems and the work.
Professional services firms don't answer to a prudential regulator, but they carry real exposure: professional indemnity claims, contractual disputes over whether a deliverable matched the statement of work, and disagreements about what was actually agreed at a milestone sign-off. That risk is mostly contractual and reputational; privacy and other statutory obligations still apply. The governance discipline Airclerk applies - review gates, source evidence, retained sign-off records - is aimed at being able to show what was agreed, what was delivered and who checked it, which is the kind of evidence that often matters most once a scope dispute or PI claim is underway.
Indirectly but usefully. When proposal templates, delivery playbooks, review checklists and precedent engagements are connected as approved sources for a governed workflow, that knowledge becomes searchable and reusable by the team, not just held in one senior consultant's head. It doesn't replace the judgement a departing lead takes with them, but it reduces how much of the firm's delivery method depends on any one person's memory.
Bring the part of the engagement cycle that costs your leads the most unbilled time. A free introductory conversation is enough to say whether it is a first workflow and to quote a fixed fee for building it.