Report & document automation
Turn manual reporting into automated, reviewed reports.
For advisory, valuation, and professional-services firms: one pipeline turns your in-house knowledge, your connected systems, and a defined report scope into branded, human-reviewed reports — delivered. Start with one reporting workflow and a rough value estimate.
Your knowledge in · Human-reviewed · Delivered under your brand
Microsoft-certified data engineer · MarkLogic Certified Administrator & Developer — credentials
Reporting is where most teams start. The same governed pipeline also covers knowledge management, document generation, and data workflows across your stack — see all services.
How the pipeline works#
Knowledge, data, drafting, branding, review, and delivery run as one governed pipeline — not a chain of separate tools you stitch together.
Your policies, precedents, and know-how — governed and current.
The source systems your data already lives in.
Exactly what this report must cover — agreed up front.
Sources and scope assembled into a first full draft.
Your templates, tone, and format applied automatically.
A person proofreads and approves before it ships.
A branded, reviewed report — ready to send.
Every stage names a result, not the engine behind it — and nothing ships without a person's sign-off.
June 2026 build — proof of concept. This exact pipeline ran end to end on a single public property listing — draft generated, corporate branding applied, human-reviewed — with public listing data standing in for your connected-systems input. The result: a complete draft analysis and a branded explainer in both English and Chinese. The same pipeline produces your financial, compliance, or operations reports. See the finished outputs →
Report · Deck · Video
Illustrative proof of concept on public data — not a certified valuation, nor financial or investment advice. Estimates are point-in-time, based on public data available as at the generation date.
Start with the value check#
Pick a repeated process with manual work, delays, rework, knowledge gaps, or missed opportunity.
Estimate volume, effort, rework, margin impact, data readiness, and complexity. Precision can come later.
The result suggests whether to pause, diagnose, map value, pilot, or manage a larger AI workflow program.
The free discovery call starts with your workflow and value assumptions, not generic AI theatre.
Quick Value Check
Estimate one workflow before you book.
Rough estimates are fine. This tool runs in your browser and does not send, store, or submit the entries anywhere.
Indicative Result
Recommended next step
Thank you. You can now book a free 30-minute discovery call. If the workflow looks promising, I may ask for the fuller self-value assessment before or immediately after booking.
Book a free discovery callFuller Assessment
Use this when the workflow looks promising.
The fuller assessment prepares the discovery call around real value. It should include the workflow, current volume and effort, estimated business value, one proposed success metric, baseline data, systems involved, risks, and questions for the call.
Privacy Boundary
Keep sensitive details out of public tools.
Please do not submit sensitive personal information, confidential customer data, regulated information, passwords, secrets, or proprietary documents in website forms or public AI tools. Use summaries and rough estimates.
Sample prompts for your own notes
Describe the workflow
I want to assess whether an AI workflow could create value for my business.
Help me describe this workflow clearly:
- workflow name
- department or team
- who performs the work today
- how often it happens
- systems, documents, emails, or data involved
- current pain points
- what a better version would look like
Ask follow-up questions, then produce a clear workflow summary.Estimate time and cost
Use this workflow summary:
[paste summary]
Ask me for missing numbers, then estimate people involved, hours per week, fully loaded hourly cost, monthly labour cost, annual labour cost, and conservative/moderate/optimistic time-saving estimates.Define the success metric
Help me define one primary success metric for this AI workflow.
Suggest the metric, current baseline, target improvement, data source, measurement period, risks, assumptions, and exclusions.Where value usually appears#
These are the high-volume, repeatable jobs that slow report- and document-heavy teams down most — reporting, document assembly, and review.
Time savings and capacity
Reduce repeatable manual effort so the team can handle more work without adding headcount.
Good first workflow:Monthly reporting, document packet assembly, recurring checks, intake triage.
Faster turnaround
Shorten response, review, reporting, quoting, or processing cycles where delays have visible business cost.
Good first workflow:Quote preparation, approval packs, customer response drafts, compliance review queues.
Error and rework reduction
Cut avoidable checking, copy-paste mistakes, formatting issues, missed handoffs, and inconsistent review.
Good first workflow:Document checks, data extraction, exception notes, policy or brand compliance review.
Knowledge access
Make policies, procedures, past work, documents, and decisions easier to find and apply with human review.
Good first workflow:Governed internal assistants, searchable knowledge repositories, evidence-backed answers.
Packages#
A$3k-A$6k
AI Quick Diagnostic
Check whether a smaller or uncertain opportunity is worth pursuing.
- Light workflow review
- Rough value estimate
- Fit/no-fit recommendation
A$7.5k-A$18k
AI Value Finder
Find the safest, highest-value AI opportunity before committing to a build.
- Workflow value map
- Risk and feasibility review
- Roadmap and prototype sketch
A$25k-A$75k
Agentic Value Pilot
Prove one AI workflow creates measurable value with real users and human approval points.
- One working pilot
- Baseline and before/after measurement
- 30 days of support
Retainer
Managed AI Value Partner
Operate, monitor, improve, and govern AI workflows over time.
- A$30k-A$120k setup
- A$8k-A$30k/month
- Optional capped value share with sunset
Optional success fees or value-share terms apply only where value can be verified: they require verified baseline data, an agreed data source, one primary success metric, attribution rules, exclusions, a written value measurement schedule, caps, review periods, a dispute-handling process, and professional legal/commercial review. They are not calculated from website inputs.
How I work#
We start with the workflow, baseline, risk, and value case before choosing a model or tool.
I build with tools your team can support: Microsoft 365, Python, APIs, KNIME, MarkLogic, local models, or existing systems.
Human review, permission boundaries, documentation, and handover matter as much as the automation.
From your knowledge to a finished report#
Your reports are only as trustworthy as the knowledge behind them. Xcelerent builds you a governed knowledge layer that answers from your own material — every answer traces back to a real source, and your confidential information stays under your control. Report automation fuses that layer with external public research, and DocMark renders the branded, human-reviewed result.
Most “AI knowledge” tools generate confident answers. Ours retrieves and cites real ones — and keeps your confidential material out of reach by design.
Most of that knowledge is messy and unstructured, not a tidy database. The knowledge layer is built for the sources real teams keep their know-how in — documents, internal wiki and Confluence pages, PDFs, SharePoint pages, and presentations — not just a single structured dataset.
Product names are referenced for compatibility and description only and do not imply partnership, endorsement, or certification by Microsoft, Atlassian, or any third party.
Communications such as Microsoft Teams chats and email are handled with deliberate restraint. We never vacuum up inboxes or chat history. Any communications source is included only with your explicit authority, scoped access, and human-reviewed governance — and only where you choose to.
Branded output, automatically — powered by DocMark
DocMark turns the structured result into branded, formatted, verified deliverables — reports, decks, spreadsheets, and documents — so the last mile of formatting and brand compliance is automated too.
Explore DocMarkCommon questions#
You're a solo practice — what happens to the systems you build for us?
You own what I build. Transfer, not lock-in: documentation and handover are part of the scope, not an extra invoice. I build with tools your team can already support — Microsoft 365, Python, APIs, KNIME, MarkLogic, or your existing systems — so the system stays yours to run. Governance, documentation, and handover matter as much as the automation itself.
How do you keep our confidential data safe?
Your confidential information stays under your control. The knowledge layer is deployable in your own environment or a private, isolated instance, with local inference when the data cannot leave the building, and your documents are never used to train third-party models. Assistants run read-only by default, scoped to each person's permissions, with every query logged. I never vacuum up inboxes or chat history — any communications source is included only with your explicit authority, scoped access, and human-reviewed governance.
How is pricing structured — is it a day rate?
No. Agentic AI engineering is priced around the value case, delivery risk, and scoped outcome — I don't sell the work as developer hours. A light diagnostic and a workflow value map can come first, so you see the value case before committing to a build.
What if AI turns out to be the wrong tool for our workflow?
I'll say so. The right answer may be no fit, a smaller diagnostic, deterministic automation, data cleanup first, or revisiting the workflow later. The value check is built so you book only if it fits — and if there's no sensible first slice of work, I'll tell you.
How do we get started, and what happens on the first call?
Start with the value check above, then book a free 30-minute discovery call if the opportunity looks real. The first call is no charge and no pitch deck: it starts with your workflow and value assumptions, not generic AI theatre. We work out whether there's a useful workflow to build and what 'observe first' would look like — an observe → design → build → transfer engagement for your context.
Start with one workflow.
Use the value check above, then book a free 30-minute discovery call if the opportunity looks real.
Book a free discovery callOr send a message · email lingtao@xcelerent.com · Sydney, Australia