● The AI analytics department

Five times the analytics. Same team. Same standards.

AI runs your analytics back office: intake, curation and analysis. Your analysts set the standards, approve what goes out, and spend their time on judgement instead of pulling data.

Book a scoping workshop → See the live demo ↗

50+Services ready day one

48+Data model tables

3Control towers

REQ-2481 · run 1Running

via email · from: brand-lead@client · 09:14 Why is TikTok down in September?

IntakeMatched “Ad hoc question”, qualifiedAI
Plan4 steps, brand × market × weekAI
QueryGoverned SQL · model v14AI
CheckData QA · freshness, nulls, CPM jumpAI
WriteNarrative + critic passAI
ReviewApproval gateAnalyst
DeliverReply on the email threadAI

Software

The DataInc Platform

A governed analytics back office. Ask by email, Slack, text, Jira or Asana; agents plan, query, check and write; your analysts approve.

Explore the platform → Tools

Control Towers

Marketing mix modeling, global media taxonomy and experiments, each running on the same core and the same data model.

See the control towers → Services

Consulting & Advisory

Scoping, system setup and senior measurement advice. We build the processing from your source data and run alongside your team.

How we work with you →

The question

What if your analytics team could do five times more than it does today?

Not by hiring five times the people, and not by handing the business a self-serve dashboard nobody trusts. The same team, with the same standards, each analyst running five times the governed work.

Bottleneck 1

Asks arrive as noise

A one-line question, a forwarded thread, a spreadsheet. Each needs back-and-forth before work starts.

Bottleneck 2

Analysts pull data

Skilled people spend their hours on extraction and checks that a system could do.

Bottleneck 3

Everything is bespoke

Every answer is built from scratch. Little carries over to the next ask.

Bottleneck 4

Recurring work never stops

Weekly and monthly reports take capacity every cycle, forever.

From · To

Five shifts your analytics team will feel. In the first month.

Chasing business partners for ticket submissions and context on every request→Ask in plain language, by email. Steward and Concierge agents qualify every request and control intake.

Analysts running their own queries, with errors slipping through→Always-on quality controls. Every query traceable, every piece of SQL governed.

Rebuilding every analysis from scratch→A repeatable system. Re-run on restated data, on old definitions or new ones.

Assumptions, context and judgement questioned every time→Agents with full context. The best coach at every analyst's fingertips.

Worrying that taxonomy shifts will break the analysis→Ontology and semantic layer control. Numbers that stay stable and earn trust.

Platform · How it works

Ask the way you already do. The answer comes back the same way.

A world-class analytics department at your fingertips, on an interaction model that is surprisingly old: you ask the analytics team, and the analytics team answers.

1 · Business asks

Any channel

EmailSlackTextJiraAsanaWeb

A one-line question, a forwarded thread or a spreadsheet.

2 · AI runs the back office

Intake

Understand the ask, match it to a service, ask the qualifying questions once.

Curation

Governed data, metric definitions and the right history.

Analysis

Plan, query, check, write and critique.

3 · Your analysts

Stewards and final approvers

Set the definitions, standards and autonomy. Approve at the gates the risk calls for.

4 · Delivered

Where you asked

A reply on the original channel, with the analysis attached
Or a tool to work in: a dashboard, a validation workbench, a control tower

The lifecycle of an ask · every run feeds the next

01AskAny channel
02IntakeMatch, qualify
03PlanAgent plan
04QueryGoverned SQL
05CheckData QA
06WriteDraft, critic
07ReviewYour gates
08DeliverChannel or tool
09RememberHistory
10RepeatSchedules

AI agentsYour analystsEvery run pins its workflow, definitions, model version and dataset

Lever 1

Intake without back-and-forth

Qualifying questions are asked once, by the AI, in the channel. The ticket arrives complete.

Lever 2

First draft by agents

Plan, SQL, data checks, narrative and critique are done before an analyst looks.

Lever 3

Review by exception

Review settings decide which steps need a person. Runs like ones already approved pass on their own.

Lever 4

Recurring work runs itself

A delivered report becomes a schedule with locked SQL. It waits for fresh data and pauses when definitions change.

Lever 5

Validation becomes a queue

Data checks are rules plus one-click confirmations, not investigations.

Lever 6

Knowledge compounds

Every run adds to the team's history. Examples and definitions carry over to the next ask.

The 5× is a target. We prove it per client against a measured baseline.

Governance

When someone asks why a number changed, you can answer in minutes.

Data model

One canonical model

Your marketing, media and customer model, versioned and approved in-app. A change shows its impact before it lands.

Pipelines

Governed like code

Branches, change requests and checks, then approval by someone other than the author. A change that moves a scheduled report pauses it for re-approval.

Standards

Written, versioned files

Metric definitions, house style, service workflows and history. Every agent uses them on every run.

Autonomy

Review matched to risk

A person designs every step, but analysts govern only the required steps. Set per service, category, group and person.

Access

Access by role

People see the categories of work their role covers. Their own and assigned work is always visible.

Audit

Full audit trail

Every decision, edit, merge and approval is recorded. Every run pins the versions it used and can be re-run.

Control towers

Tools that put the model to work on your hardest recurring problems.

Each tower is a service in your catalog with its own screens, recurring runs and experts who confirm. Underneath sit the same requests, gates, governance and channels.

Tower 01

Marketing mix modeling

Built

The client. A multi-brand advertiser refreshes its MMM quarterly through an agency. Every refresh starts with weeks of input wrangling: spend that doesn't reconcile to finance, a channel that went dark, a tracking change nobody logged.

Define the inputs once. National, attributed and geo datasets live in the data model. An agent writes the spec; a data admin merges it.
Validate every week. Coverage gaps, missing weeks, cost jumps, level shifts, the attribution identity, reconciliation and a sanity forecast.
Predict every gap. Missing weeks, late feeds and channels that went dark get a forecast fill, clearly flagged and replaced when actuals arrive, so the model never runs on holes.
Explain every dip and spike in the workbench, drilled down to the creative, with the cause logged.
Publish and sign off a version: frozen, exported to the modelers or agency, compared with the last one.
Ask anything about it. “Why is TikTok down in September?” is answered against the same governed data.

How we measure value

Days from data cut to sign-off

Anomalies explained up front

Reconciliation gaps found

Refresh: quarterly to monthly

Tower 02

Global media taxonomy

Built

The client. A global brand buys media in 30 markets on 8 platforms through several agencies. A fifth of spend can't be cut by brand, market or objective without manual cleanup. Every global report starts with a mapping spreadsheet.

Rebuild and validate on a schedule: daily validation, a weekly rebuild from raw sources, and whenever a pipeline change merges.
Scan the structure. Can every number be placed from campaign to placement, ad group and creative? Unmapped IDs, missing POs and new sources, each with spend at risk.
Scan the names. Every name at every level is checked against the global convention for misalignments, gaps, typos and placeholders.
Route each gap to the right expert by type, platform, brand and market, with a service level and escalation.
Fix in one click, governed. The expert confirms, a data admin merges, the next run closes the gap, the platform team gets the rename list.

How we measure value

Clean-chain share (demo starts at 27%)

Mean time to fix

Gaps closed per expert-hour

Reports built straight from data

Tower 03

Experiments

Built

The client. A retailer runs dozens of tests a year across teams: geo holdouts, audience tests, creative splits. Designs vary, results live in decks, and the same question gets tested twice.

Take test ideas from any channel. “Can we test pausing TikTok in Texas?” The AI qualifies the hypothesis, KPI, unit, duration and effect size.
Design to one standard: assignment, power calculation and pre-period checks. The analytics lead approves at a gate.
Monitor daily while live: balance, contamination, spend actually paused in holdout markets, sample-ratio mismatch, freshness.
Read out on one method, with confidence intervals. The narrative is drafted and held at the analyst's gate.
Feed the models. Approved lifts become priors for the MMM tower. Every design and read-out is searchable for the next ask.

How we measure value

Tests per quarter per analyst

Idea to approved design

Tests with a valid read-out

Lifts reused as MMM priors

Why we move fast

The catalog and the data model already exist. We only need your source data.

The catalog

50+

Analytics services ready on day one, each with its intake form, workflow and review rules already built and mapped. Your custom tasks are appended.

New reportAd hoc questionQBRData extractFix a data loadMMM input checkTaxonomy healthAnomaly scanTest designTest read-outRecurring reportNaming auditReconcile spend+ Your task

The data model

48+

Tables for marketing, media and customer data, versioned and approved in-app. Every service and every control tower reads the same tables.

You provideMeta, TikTok, Google, ad server, CRM, finance and POs, plans We buildThe processing workflow: governed pipelines your team approves Already thereData model, catalog, control towers

Consulting & advisory

Senior analytics people, working with the system and with your team.

Software alone doesn't change how a department works. We bring measurement and analytics-engineering experience to scope the work, stand up the platform on your data, and advise your leaders as the operating model changes.

Strategy

Analytics operating model

Where AI should and shouldn't touch your numbers. Catalog design, autonomy levels, review gates and the roles of stewards and approvers.

Measurement

Marketing mix modeling

Input design, validation standards and sign-off, plus partnering with your agency or in-house modelers on refresh cadence.

Measurement

Experimentation programs

Test design standards, power and method choices, read-out governance, and calibrating MMM with approved lifts.

Data

Media taxonomy & governance

Global naming conventions, expert routing and service levels, and a semantic layer that survives taxonomy change.

Engineering

Data model & pipelines

Mapping your sources to the canonical model and building governed processing your team owns.

Change

Adoption & value tracking

Baseline measurement, channel rollout and the scorecard that shows whether the gain is real.

How we engage

Three ways to work with us. One way to start.

Step 1 · Every engagement

Scoping workshop

We map your sources, priority services and the baseline to measure against.

4 weeks · $20,000 fixed

Step 2

System setup

We build the processing workflow from your source data and configure the catalog, standards and people.

1 to 8 weeks · time & materials

Step 3 · Run · choose a model

Capacity on demand

We run in the background. Your team uses the system when demand goes over capacity.

Pay only for the hours used

Alongside your team

Your people work in the system with us. You validate and track the output.

Pay for hitting agreed efficiency benchmarks

Subscription

Every task run in the month costs the same. Choose the service level that fits your budget.

$100 per user, per month, per tool, plus tokens

Models A and B deploy on your cloud provider, with your compute and your LLM API keys. Model C is available as full SaaS with per-user licensing, on your API keys or ours.

Proving the 5×

The 5× is a target until your numbers prove it. We measure from day one.

Your value scorecard

Requests per analyst per monthVolume
Median time from ask to deliverySpeed
Share of runs that needed no reworkQuality
Share of recurring work automatedCapacity
Control-tower health: clean chains, signed-off datasets, valid read-outsTowers

The risk

Five times is the target. Your volumes, data quality and adoption decide the result.

How we handle it

A baseline month before go-live, the same measures after, and expansion only on proof.

Get started

Start with your source data. We build the rest.

Decision 1Which sources we connect first

Decision 2Who owns it on your side

Decision 3When the baseline month starts

Open the live demo ↗

● About DataInc.ai

We build the analytics department we always wanted to run.

DataInc.ai combines a governed AI platform with senior measurement and analytics consulting. We exist so analytics teams can spend their time on judgement, and so the business can trust every number it acts on.

Why we started

Analytics teams don't need more dashboards. They need a better operating model.

Most analytics capacity goes to intake, data pulls and recurring reports, so the work that needs expertise waits. We think the fix is to give the back office to AI agents under the rules analysts write, and keep people in charge of standards, review and the final answer.

We believe

Analysts keep the final say

AI drafts, checks and chases. People set the definitions, approve at the gates and own what goes out.

We believe

Every number should be traceable

One data model, governed pipelines and pinned runs, so “why did this change?” takes minutes.

We believe

Value has to be proven

A baseline month, the same measures after go-live, and expansion only on proof.

Leadership

Led by practitioners who have built analytics teams at scale.

The team

The people behind DataInc.ai. Growing with our clients.

Measurement scientists, analytics engineers and AI builders. Filter by group to see who works on what.

Work with us

Start with your source data. We build the rest.

Talk to us → Join the team →