Seconds for simple questions. Minutes for the ones that take an analyst hours. All from your own data.
Opens a scripted preview that answers from this page. Nothing is sent to a model.
Who it's for
Answers leaders can act on. Without the wait for their teams.
For leaders
Finance, operations, and business unit leaders
Faster decisions. Lower cost. No new risk.
For your teams
Operations, finance, marketing, analysts, and data teams
Hours back every week, and numbers you can trust.
From question to decision, faster and cheaper.
Today
Hours to days, through an analyst
With dataface.ai
Seconds for simple questions, minutes for hard ones
Today
Seats, a warehouse, pipelines, and people to run them
With dataface.ai
One platform fee
Today
Records copied into warehouses and exports
With dataface.ai
Your tables stay where they are
Today
Months of data work before the first chart
With dataface.ai
A pilot on your own data in weeks
Every industry below runs on a working demo of that business. Pick yours to see the problem, how dataface.ai helps, and what changes.
The question on the table
“Which payer drove the denial spike last quarter, and what is it costing us?”
What it costs today
Denials surface at month-end, spread across claims, remittances, and payer contracts. By the time someone reconciles them, the appeal window is closing and the cash is gone.
How dataface.ai helps
Recover revenue sooner and bring days in A/R down.
No training course, no dashboard backlog, no data project first. Just the answers people have been waiting for.
No SQL, no dashboard request, no training course
Type the question the way you would ask a colleague. dataface.ai finds the right data, works out the answer, and replies with the number and a chart. Ask the follow-up straight away.
Which payers have the highest preventable denial rate this quarter?
Numbers you can put in front of leadership
When a question could mean two things, it shows the options and asks which you meant, instead of quietly picking one. Every answer shows the steps it took to get there.
"Denied coverage" could mean the denial category, the denial reason, or a prior authorisation outcome. Which one?
The right picture for the answer, automatically
Trends come back as line charts, comparisons as bars, a single figure as a headline number. Sort, switch views, and take it straight into the meeting.
On-time delivery by month, with the April dip and the freight spend spike side by side
Most analytics tools start by copying your data somewhere new: a project to build it, and one more place to protect. dataface.ai asks your existing systems directly.
No data warehouse to fund. No pipeline project to wait for. No second copy of your tables for your security team to worry about.
Built for healthcare, banking, and every business whose data is not allowed to wander. Your security team keeps control, and nobody has to make an exception for us.
Your databases, business apps, cloud storage, and the spreadsheets in between. Nothing gets moved or rebuilt to start asking questions of them.
Postgres, MySQL, Oracle, SQL Server
Amazon S3, Azure, Google Cloud
CSV, Excel, JSON, and more
Kafka and real-time feeds
Salesforce, Stripe, Zendesk, any API
dataface.ai is a member of these startup programs. They put cloud infrastructure, security review, and enterprise introductions behind the product our customers run.
Cloud program: infrastructure credits, technical review, go-to-market support.
Founders Hub: Azure and OpenAI credits, enterprise co-sell motion.
India's flagship innovation hub, Hyderabad: mentorship and enterprise access.
Enterprise engineering platform and open-source ecosystem access.
Program names and marks belong to their respective owners.
No per-seat licensing. Unlimited users and agents on every plan: you pay for governed execution, not for how many people log in.
Prove the value on one real business question, with your own data
Give every business unit direct answers from its own data
For regulated teams that need everything to run inside their own network
Have procurement, residency, or private deployment requirements?Start a pricing conversation
Legacy BI taxes every person who logs in. Warehouse-native AI meters every question you ask. dataface.ai does neither.
dataface.ai
Ask anything, as often as your team needs. No per-question charge, ever.
Snowflake Cortex Analyst
Metered on every message, before the warehouse compute, storage, and ETL it runs on.
We don't put a meter on curiosity. One platform fee, your team asks freely, and only the answer ever leaves your data.
| Annual cost, business analytics | 25 users | 250 users | What you still need underneath |
|---|---|---|---|
dataface.ai BusinessNo seats One all-in number, scales with usage, not headcount | ~$18,000 | ~$70,000 | Nothing. It runs on the sources you already have. |
| Looker Platform fee + per-user licenses | ~$72,000 | $250K–$800K | Warehouse compute, ETL, a LookML modeler |
| Tableau Cloud (Enterprise) Per-seat: Creator / Explorer / Viewer | $23,700 | ~$237,000 | Warehouse, extracts, an analyst per dashboard |
| Tableau Cloud (Standard) Per-seat: Creator / Explorer / Viewer | $14,580 | ~$145,800 | Warehouse, extracts, an analyst per dashboard |
| ThoughtSpot Per-user; real contracts run far above list | $15,000 | ~$137,000 | Warehouse compute, pre-modeled tables |
| Power BI Pro Seats only: the cheapest sticker in BI | $4,200 | $63,090 | Fabric capacity or a warehouse, ETL, a report builder |
Illustrative, from publicly listed pricing (July 2026). Competitor figures are license and compute list prices only, so the right-hand column is what the sticker leaves out. Talk to us for a scoped quote.
Security, access control, accuracy, pricing, and time to value: the questions that actually decide this, answered in full rather than promised in a follow-up. Ask in your own words with Quick check, or open any answer on its own page.
dataface.ai is on-source conversational analytics. Ask a question in plain English, and it generates read-only SQL from your schema metadata and runs it on the systems you already own. No warehouse copy, no pipeline.
dataface.ai reads your schema and a few example values per column, generates read-only SQL, and executes it on the source system itself. Your tables are never copied: no pipeline, no warehouse copy, no second place to secure.
By default the model sees your catalog: table names, column names, types, and a few example values per column. It never receives your tables, and it only sees query results if a user turns on AI analysis.
Generated SQL is SELECT-only. INSERT, UPDATE, DELETE and DDL are blocked, queries are parameterized against injection, and every request passes both a workspace gate and a datasource gate.
Yes. Enterprise agreements cover dedicated, VPC, and on-premise deployment, with edge execution on your own infrastructure at a reduced credit rate.
An annual platform fee plus execution credits. Users are unlimited, because charging per seat would tax the exact behaviour the product exists to create.
Roughly 80% lower analytics cost by removing pipelines, warehouse storage and duplicate copies, and around 40% higher operational efficiency because teams self-serve answers instead of queueing for analysts.
It asks. When a question has no clean mapping, dataface.ai shows the candidate columns it can actually see and lets a human choose, rather than guessing and returning a confident wrong number.
Ten databases and warehouses are live, including PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, Snowflake, BigQuery, Teradata, Cassandra and Amazon S3, alongside API connectors and direct file upload.
Three layers: refuse to guess at question time, record every model call in an append-only ledger, and score releases against golden question sets in a standalone evaluation service.
Separate workspaces per team, project or client, and every query passes both a workspace gate and a per-datasource permission gate before it runs.
Regulated, data-heavy operations: healthcare and revenue cycle, financial services, aviation, retail and e-commerce, and supply chain, with AI tuned for those domains rather than generic.
Connect a source, ask a question, get an insight. A formal evaluation runs as a scoped pilot of eight to twelve weeks against one governed use case with your own data.
The people behind dataface.ai
Founder & CEO
Software engineering leader with nearly 19 years of breakthrough career in building and leading high-performing software development teams for the design and delivery of large-scale software products for Data, ML, Enterprise Applications & Cloud Solutions.
Ratnaker's LinkedIn
