Agree the source of each number.
Specify the report audience, source files, reporting period and metric definitions. Prepare approved example answers and missing-data cases.
Scope, cost and delivery
One recurring report or document workflow
A pilot with representative inputs, explicit calculations, source references and a human approval step.
What determines the quote?
Input quality; source count; model and tool access; output format; evaluation sample; review time; run costs and maintenance.
The proposal separates discovery, implementation and ongoing support. Software licences, hosting and usage charges are identified separately. We confirm the price against your samples and acceptance criteria before implementation starts.
Planning the first engagement
Indicative planning ranges for the focused scope above, starting once access and samples are ready. These are estimates; the proposal confirms dates and dependencies.
- Input, output and acceptance-sample review2–5 business days
- A repeatable workflow evaluated against approved examples1–3 weeks
- Reviewer acceptance and a run-cost baselineAgreed after pilot
Review a source-to-report example
The Excel-to-presentation tutorial shows the validation and review steps. Bring a redacted reporting pack to assess your own workflow. Tutorial examples are not a promise of accuracy or time saved.
Excel-to-presentation workflow →Before you commit, agree which sample records, screens or reports the pilot must demonstrate and who will accept them. Any before-and-after result needs an agreed baseline and measurement period.
Describe the report or document, its source files and who approves the final output.
Review my AI reporting workflow →Review a first milestone before payment.
Agree a small deliverable, fee and acceptance checks in writing. Review the work, pay after acceptance and decide whether to continue. Remote delivery in English, led by Raghav with The Night Marketer.
Synthetic reporting example
Trace a reporting slide back to four source rows
Use the same small input, prompt and answer key to evaluate a reporting workflow. The figures below are calculated reference answers, not captured Claude output or a client performance result.
Scroll the example table sideways to read every column.
| Stage | Example | Review decision |
|---|---|---|
| Input | Two periods, two channels; net sales in INR with refunds already deducted. | Check the date window and metric definition before asking for a narrative. |
| Checked calculation | Prior: 100,000 + 50,000 = 150,000. Current: 120,000 + 45,000 = 165,000. | Change = 15,000 ÷ 150,000 = 10%. Do not subtract refunds again. |
| Draft slide claim | Net sales rose 10%; the direct channel grew while wholesale declined. | Supported by the sample. “Advertising caused the increase” is unsupported and must be removed. |
| Approval | Figures checked; causal explanation unresolved; presentation still a draft. | The named reviewer checks sources, chart labels and wording before sharing. |
The sample passes arithmetic checks, but the final deck still needs visual review. Compare your generated file against the answer key; a valid total does not prove that its narrative, chart or layout is correct.
- Download all example files (ZIP)
- Reporting input (CSV)
- Original slide brief and prompts (TXT)
- Expected figures and review checklist (JSON)
- Reproduce the arithmetic checks (Python)
To reproduce the offline checks, extract the complete pack and run python3 verify_examples.py in that folder. Python 3 is required; the checker uses no network connection.
How does an AI reporting workflow keep numbers traceable?
Excel, PowerPoint, documents, approved business exports, and AI tools can be combined around the reporting task. File support, account permissions, model access, data handling, and available integrations are checked during discovery.
Prepare inputs
Identify the reporting period, source exports, required fields, and the person who approves the data.
Calculate and validate
Use explicit calculations for totals and comparisons. Flag missing or inconsistent inputs before drafting.
Draft with sources
Provide the checked figures to the model and keep references alongside each narrative claim.
Review and deliver
A reviewer checks claims and presentation quality. Record corrections and approve the final deck or document.
What happens when the normal path breaks?
Illustrative design: a monthly reporting workflow receives sales and advertising exports for different date ranges. It stops the comparison and requests aligned inputs rather than drafting a growth claim. Once the data is checked, AI prepares the narrative and a reviewer approves the deck. This example describes intended behaviour, not a client outcome.
Which implementation approach fits your team?
| Approach | Good fit | Check before committing |
|---|---|---|
| Reusable prompt and template | A person prepares consistent inputs and reviews each output. | Keep source references, a review checklist, and versioned instructions. |
| Workflow integration | A recurring report needs collection, validation, drafting, and routing. | Define missing-data behaviour, retries, permissions, and the approval step. |
| Custom implementation | Several data sources or business-specific calculations and controls. | Agree evaluation samples, operating costs, maintenance, and failure handling. |
Swipe sideways to see all columns.
What should you prepare before a pilot?
- Choose one recurring report and an accountable reviewer.
- Collect redacted inputs and an approved example output.
- Define calculations separately from narrative drafting.
- Check periods, totals, missing values, and source references.
- Evaluate corrections and completion time on representative samples.
- Document approval, recovery, and ongoing ownership.
Learn about The Night Marketer team and read its Shopify Partner Directory profile.
The pattern behind the request
Where can an apparently polished report be wrong?
01
A polished slide contains figures nobody can trace to a source
02
Generated commentary invents causes or hides missing data
03
Every reporting cycle needs the same manual checks and corrections
What this service makes easier
Use AI practically inside business workflows: Excel analysis, PPT reporting, SOPs, dashboards, content, and team documentation.
- Claude with Excel workflows for analysis, cleanup, and reporting
- Claude with PowerPoint workflows for review decks and client reporting
- AI-assisted SOPs, briefs, summaries, and operating documentation
- Human-in-the-loop automation so quality stays controlled
A clear path, not a black box
From source files to a reviewed reporting workflow
Start with one recurring report and a known answer set. Separate calculations, generated commentary and the approval that makes the output ready to share.
Check numbers and unsupported claims.
Compare calculations with reference answers. Require source references and send missing evidence or ambiguous instructions to review.
Make review repeatable.
Give the operator a prompt, input contract, approval checklist and recovery instructions. Measure run cost and reviewer effort during the pilot.
What gets built
Input contracts, answer keys and an approval step
The pilot should make it clear which work is deterministic, which text is generated, and which decisions remain with the reviewer.
source files and metric definitions
calculation checks and generated commentary
reviewer approval and reporting handover
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01
Workflow use-case map
Select one recurring task with a named audience, owner and acceptance definition.
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02
Prompt and data input templates
Supply a sanitised input format, precise metric definitions and reusable instructions.
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03
Excel/PPT/doc automation setup
Evaluate a working report against normal, missing-data and contradictory-input cases.
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04
Team training and governance notes
Document reviewer responsibilities, escalation, running costs and maintenance.
Direct access, execution depth
You get one person to own the outcome, backed by a team that can make it real.
I diagnose the problem and stay responsible for the path to delivery. When the work needs design, engineering, SEO, automation, or specialist support, The Night Marketer team adds depth without making you coordinate the room.
- A concrete scope before a long project starts
- Decisions and exceptions documented as the work takes shape
- Testing and handover built into delivery, not left for the end
Common questions
Useful answers before we talk.
What should we automate first with AI?
Start with a recurring task whose inputs, output, reviewer, and quality criteria are clear. Reporting drafts and operating documentation are easier to evaluate when an approved example and source data already exist.
How do you measure whether an AI pilot works?
Compare representative runs with the current process. Record completion time, factual errors, reviewer corrections, failed runs, and operating cost. Agree acceptance criteria before deciding to expand the workflow.
Can our team keep reviewing the final output?
Yes. Review is an explicit workflow step. The system should make source evidence and uncertain claims visible so the reviewer can approve, correct, or return the work.
Keep exploring
AI Workflows guides and field notes
These are practical reads for understanding the job, asking better questions, and spotting the risks that are easy to miss in a first call.
