Unified reporting layer
One reporting layer over ERP, CRM, and e-commerce — month-end reporting cut from days to same-day, and one version of the truth that ends the spreadsheet arguments.
From scattered data to same-day answers.
We turn scattered data into same-day answers: pipelines, warehouses, and live dashboards that cut reporting from days to hours — and leave you the clean, governed data foundation every AI initiative depends on. First dashboard target set in a two-week audit, from a team that runs its own SaaS on live data.
01 — THE WORK
The four jobs we’re hired for.
You'd call us when month-end reporting takes days and six systems disagree — or when leadership decisions wait on numbers nobody trusts.
Live operations dashboards
Sales, inventory, fleet, occupancy — on screens managers actually watch. Decisions get made on today's numbers, not last month's.
Data warehouse / lakehouse build
Analytics stops slowing down your production systems, history stops disappearing, and you gain the ready substrate for ML and RAG work later.
Forecasting & anomaly detection
Demand forecasts, churn signals, and fraud flags surfaced early enough to act on — fewer stockouts, earlier interventions, and fewer surprises in the quarterly review.
02 — THE AI FOUNDATION
Where the AI work starts.
Every AI initiative stands on this work. RAG assistants need clean, permissioned documents; forecasting needs history you trust; agents need APIs into data that means what it says. Build the data foundation once and the AI work that follows gets faster, cheaper, and honest — it's the sequence we followed ourselves with DigiSign.
WHO BUYS THIS
CFOs with reporting pain, COOs who want operational visibility, CEOs of data-rich but insight-poor mid-market firms. Strongest in retail and distribution, logistics, fintech, manufacturing, and healthcare.
03 — HOW IT RUNS
Discover. Build. Run.
The shape every engagement takes — a short paid discovery, a build you can watch, and a run phase we operate or hand over cleanly.
DISCOVER
1–2 wks
A data audit: source inventory, quality assessment, and a quick-win dashboard target picked with the people who will use it.
BUILD
4–10 wks
Pipelines, the warehouse, and the first live dashboards — sequenced so leadership sees a working dashboard early, not at the end.
RUN
ongoing
A data-ops retainer: pipeline monitoring, failure recovery, new metrics and sources as the business asks, and governance upkeep.
04 — RELATED SERVICES
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AI & Intelligent Automation
We put AI on the workflows that eat your team's hours — and run it in production ourselves.
Digital Transformation & Enterprise Systems
A pragmatic path off spreadsheets and 15-year-old software, one measurable step at a time.
05 — FAQ
Fair questions, straight answers.
With a 1–2 week data audit. We inventory the sources, assess quality, and pick one high-value report to fix first — usually the one leadership waits longest for. You get a prioritized roadmap and a first live dashboard target measured in weeks, not a boil-the-ocean platform plan.
Off-the-shelf first. Power BI or Tableau on a clean warehouse covers most reporting needs at the lowest cost; the hard work is the data modelling underneath, which we do either way. Custom dashboards earn their keep for customer-facing analytics, operations walls, and workflows BI tools can't express.
Typically inside the first 4–10 week build — often sooner, if the audit finds a single-source quick win. We deliberately land one visible dashboard early: it builds trust in the numbers and flushes out the data-quality arguments while the rest of the pipeline work continues.
You don't clean it by hand — the pipeline does. We profile the mess during the audit, then build validation, deduplication, and reconciliation rules into the ingestion layer so data arrives clean every day, not once. Where systems genuinely disagree, we surface it to an owner instead of hiding it.
If reports strain your production database or span several systems, you need a warehouse. A lakehouse matters when large unstructured data or ML enters the picture. With one system and modest volumes, direct BI may be enough for now. The audit answers this in writing, with costs.
AI is only as good as the data underneath it. The warehouse, pipelines, and governance we build are the substrate RAG and ML need: clean, current, permissioned data with known lineage. Clients who start here skip the most common AI failure mode — a model on top of numbers nobody trusts.
Either your team, with our documentation and a proper handover, or ours, under a data-ops retainer: pipeline monitoring, failure recovery, new metrics and sources, and governance upkeep. Pipelines are living systems — upstream formats change without warning, so someone must own them.
Access is role-based and least-privilege, sensitive fields are masked or excluded at ingestion, and every pipeline logs who touched what. Data stays in your environment — your cloud accounts, your keys — under NDA. Requirements like DPDP or GDPR are scoped into the design, not patched on.
Tell us what's slow.
We'll tell you what we'd build — and what it costs to run.