What we build
Numbers + narrative + next-step. Every report.
Each report is a production artefact — warehouse-grounded numbers, AI-drafted prose, anomaly detection, driver attribution, and concrete recommendations. Composed into one delivery, not stitched from five tools.
Reports that write themselves
Daily revenue snapshots, weekly pipeline health, monthly board decks — generated from your warehouse without a human pulling SQL or assembling slides. The narrative reads like a senior analyst wrote it, because the model trained on yours did.
Anomaly detection with reasoning
Spikes, drops, and pattern breaks flagged the moment they cross threshold — not a week later in a review meeting. Each anomaly carries the reasoning: what changed, why it matters, and the most-likely root cause. No black-box alerts.
Recommendations, not just dashboards
Reports don't stop at numbers. They tell you what to do — extend cohort A by two weeks, run a CFO-touchpoint sales review, hire a second CSM. AI surfaces the pattern; you approve the action. No more 'the data is interesting' meetings.
Driver attribution + 'what changed'
Every metric movement attributed to its drivers — paid social up 28% drove revenue, a shipping delay caused the refund spike, three CFO objections stalled the proposal cohort. Stop guessing why; start fixing.
Delivered to where leadership reads
Slack channels, email, Notion pages, Google Slides — wherever your team actually consumes information. Per-recipient summaries; deeper drill-downs only one click away. Not a 47-page PDF nobody opens.
Continuously tuned · not built once
Reports get sharper over time as the model learns which insights actually moved the needle and which were noise. Feedback loops baked in — leadership flags useful insights; the model surfaces more like them next week.
Where reports land
Every cadence, every audience, every channel.
Same generation engine, tuned per cadence and audience. Daily snapshots, weekly health checks, monthly board decks, anomaly alerts, post-mortems. Composed from shared primitives instead of rebuilt per report.
Daily revenue snapshot
06:00 SGT every day in #leadership Slack. Yesterday's revenue, pipeline created, top closed-won, refund anomalies, and the one thing that needs attention today. Replaces the 'morning standup catch-up' meeting.
Weekly pipeline health
Monday 09:00 to RevOps and sales leadership. Coverage ratio, stage conversion, stalled-deal flags, AE-load distribution, win-rate by segment. Drives the weekly forecast call instead of fueling it.
Monthly board deck
Last business day, 18:00 to CEO + Board. ARR, net new, churn, NDR, expansion drivers, cohort retention curves. 11 pages of charts and commentary, not a 47-page deck nobody opens.
Anomaly + incident reports
Real-time alerts when revenue, conversion, fraud, or churn signals cross threshold. Each alert ships with root-cause analysis and the suggested next step — not just 'something looks off.'
Campaign + cohort post-mortems
Auto-generated within 48 hours of a campaign or cohort closing. What worked, what didn't, recommended changes for next time. Turns campaign learnings into compounded knowledge.
Department-specific digests
Sales digest, marketing digest, ops digest, exec digest — same data, different audiences. Each tuned to what that team actually cares about, not a one-size-fits-all dashboard.
Model families we deploy
No single model handles every report. So we compose.
Narrative writing, anomaly detection, driver attribution, and recommendation generation each run on their own model — composed into one report engine with version control at every step.
Claude Sonnet/Opus turning warehouse query results into executive-ready prose. Trained on your tone, your KPIs, and your past reports — with version control on every prompt and template.
Statistical + ML models running per metric against rolling baselines. Tags spikes, drops, and shifts above tunable thresholds. Output is the anomaly + the suspected drivers + a confidence score.
Decomposes metric movements into the contributing factors — channel, cohort, segment, geography. Claude-reasoned attribution explains the 'why' in plain English, not just a regression coefficient.
Reads the report's findings and proposes concrete actions — extend a cohort, run a sales review, ship a fix. Each recommendation has a projected impact and a reasoning trail; leadership approves before action.
Sources + delivery wired into every report
Every system that produces or consumes a number — integrated.
Read from your warehouse, BI tools, CRM, and product analytics. Deliver to Slack, email, Notion, or slides. Numbers grounded; narratives audited; reasoning trail attached to every recommendation.
Per-report explainability
Every insight cites its source. Every number traces to a query.
SQL queries used, data ranges read, model version, prompt template — captured per report. Operators replay any insight back to the warehouse query that produced it. Numbers grounded; narratives auditable.
- SQL queries cited per insight
- Model + prompt version on every narrative
- Anomaly drivers explained · not asserted
- Replayable from any historical run
Frameworks we align to
Why Axccelerate for AI reporting
Not a dashboard.
A reporting system.
A BI dashboard gives you charts. Our system gives you narrative + anomaly detection + driver attribution + recommendations + the audit trail behind every number. The layer that turns dashboards into decisions.
Pricing
Priced to your reports and your stack — not seat counts.
Reporting deployments are scoped — we cost against your reports, integrations, and review cadence before quoting.
Glossary
The vocabulary behind every report.
A quick reference for the terms that show up in report specs, narrative drafts, and review meetings — the language your analytics, RevOps, and exec teams will use during deployment.
- Narrative
- Prose summary of data
The plain-English commentary that wraps the numbers — what happened, why it matters, what to do. AI-authored on top of warehouse-grounded queries; not a hallucinated story.
- Driver attribution
- What caused the metric change
Decomposing a metric movement into its contributing factors — channel, cohort, segment, geography. Answers 'why did revenue jump?' not just 'revenue jumped.'
- Anomaly
- Pattern break above threshold
A metric reading that deviates significantly from its rolling baseline. Tunable thresholds per metric; AI suggests the most-likely root cause and routes to the right team.
- Semantic layer
- Shared metric definitions
A central definition of what each metric means — pipeline coverage, ARR, NDR — used consistently across reports, dashboards, and AI queries. dbt, Cube, Looker LookML.
- Rolling baseline
- Recent-trend reference point
The last N days of a metric used as the reference for anomaly detection. Adapts to seasonality and growth without manual recalibration; tunable per metric class.
- Cadence
- Report frequency
How often a report runs — daily, weekly, monthly. Different cadences serve different audiences and decision rhythms; AI tuned per cadence to surface the right level of detail.
- Recipient digest
- Audience-specific summary
A version of a report tuned to one audience — sales, marketing, ops, exec. Same underlying data, different framing and depth per role.
- Reasoning trail
- Per-report audit log
The full record of how a report was generated — queries run, data ranges read, model used, narrative drafted. Available for replay, debugging, and compliance review.
- Confidence
- Model self-assessment
A score per insight indicating how strongly the model stands behind it. Low-confidence insights queue for human review; high-confidence ones publish solo.
- Recommendation
- Suggested next-best-action
An action proposal generated from the report's findings — extend a cohort, run a review, ship a fix. Each carries a projected impact and reasoning; leadership approves before action.
- Approval gate
- Mandatory human checkpoint
A step that requires named human sign-off before delivery — typically used on board decks, external reports, and high-stakes recommendations.
- Drift
- Behavioural deviation over time
When a model's outputs gradually shift away from the baseline — usually due to data changes, schema evolution, or business context drift. Drift monitoring catches this before reports degrade.
Audit the report.
Ship the cadence.
30-minute scoping with a senior engineer and a reporting specialist. You'll leave with a report map, integration plan, and realistic timeline — not a sales pitch.