AI AGENT ACCURACY · MEASURED
Your AI agent answers questions from your data. Most of those answers are wrong.
AI agents answer real business questions at under 50% accuracy. The failures look plausible, so nobody catches them. We measure your score, build the open-source foundation that takes it above 85%, then prove it by re-running the same benchmark. You own everything we build.
- Benchmark methodology built on published research
- 100% open-source, zero license fees
- Fixed price, fixed scope
- You keep the eval suite either way
Q: What was net revenue in Q3?
$1,240,890
included refunded orders · used the wrong revenue definition
$891,204
0/30 correct · 0%
THE INVISIBLE FAILURE
Wrong answers don't look wrong.
An agent's answer comes back clean, confident, and plausible. Nobody re-checks it by hand. That was the point of asking. So the number goes into the board deck, the dashboard, the pricing decision.
Published evaluations put agent accuracy on real business questions under 50%. The SQL is fine. The models can't know what nobody wrote down: what your terms mean, which rules apply, where the ground truth lives.
Published evaluations, sources: dbt Labs benchmark · Cube agent-accuracy benchmark · enterprise-schema evaluations
Your systems don't say what things mean.
ord_amt, net_amount, adj_rev_usd: three revenue-ish fields, zero docs on which one is the revenue. The agent picks. Sometimes it picks right.
Your teams don't agree either.
Finance excludes refunds. Growth doesn't. Both live in your data. The agent can't know it walked into a fight.
Nothing controls what it can see.
Without compiled access rules, one customer's question can read every customer's rows. The scariest failures aren't wrong answers. They're leaks.
The failures are invisible until you measure. So we measure.
HOW WE WORK
Measure first. Then fix. Then keep it fixed.
The AI Data Accuracy Audit
One week · fixed scope
Thirty of your real business questions, benchmarked against your verified ground truth.
- • Your accuracy score, measured, not guessed
- • Every failure classified: wrong definition, wrong logic, wrong scope, policy or access leak
- • A prioritized fix plan with effort estimates
- • The eval suite is yours to keep, whatever you decide.
The AI-Ready Data Foundation
Scoped from your findings
The fix, scoped from your audit findings. Never sold cold.
- • Cleaned, documented, tested data models (dbt)
- • Every metric defined once in version-controlled open source (Cube / dbt). The fights about which number is right get settled in writing.
- • Read-only access with row-level rules compiled into every answer.
- • The benchmark re-run, so the improvement is a number, not a promise
- • Full documentation and team training
The Accuracy Retainer
Monthly · cancel anytime
Accuracy decays. Systems drift, metrics get added, models update.
- • New metrics modeled as your business changes.
- • Re-benchmarks on every major model release.
- • Drift alerts and a monthly accuracy report
- • Quarterly review with your team
NO LOCK-IN
You own everything. We license nothing.
Everything ships as code in your repos: models, definitions, access rules, dashboards, the eval suite. All open source, zero license fees. If we disappear tomorrow, your team runs it without us. That has been the rule since day one.
dbtTransformations
CubeMetrics
ClickHouseAnalytics DB
PostgresDatabase
GrafanaDashboards
MCPAgent accessOpen-source tools you keep. Zero license fees.
THE AUDIT, DAY BY DAY
One week from “we think” to “we know.”
- 1
Day 1
You share 30 real questions and a read-only replica or masked snapshot. We never touch production. We never get write access.
- 2
Day 2
We verify ground truth for every question with your team. Most clients learn something before any agent runs.
- 3
Day 3 to 4
We run the benchmark: raw setup, then a documented, governed version, then your existing tooling if any. Every answer logged, scored, classified.
- 4
Day 5
You get the report and a walkthrough: score, failure taxonomy, fix plan. The eval suite moves to your repo.
TRACK RECORD
The foundations underneath, already built.
Accuracy measurement is our focus for 2026. The foundations under it are what we've always built.
Real-time data platform for a UK parking-management company
SUB-SECOND QUERIES · 100% SELF-HOSTED
Read case study →Foundation workAgent behavior made measurable for an AI platform
DEBUGGING TIME DOWN 80%
Read case study →Foundation workLegacy database to self-service answers
REPORTS 10× FASTER · ZERO LICENSE COST
Read case study →“Primastat's work is brilliant and their attention to detail, especially considering the complexities of our requirement was excellent.”
Alwyn Veliyeth
CTO, Rezcomm
“Primastat's data analytics transformed our business, delivering actionable insights and driving exponential growth.”
Tushar Aggarwal
CEO, TUAG
“Primastat helped us creating efficiencies in our internal processes using various AI approaches. The team is responsive and consultative to address requirements.”
Raja A.
Delivery Head, Dsquare
Questions we get.
It already does, confidently, all day. That's the problem: published evaluations score it under 50% on real business questions, because your definitions and ground truth were never written down where it can see them. We don't compete with the agent. We build the layer of verified definitions it needs, and prove it with a before/after score. Think you're already accurate? The audit is the cheapest way to be sure.
Good. They'll own everything we hand over: documented, version-controlled code. Two questions first: has anyone measured your current accuracy? And what falls off their roadmap during the six to eight weeks this takes to build from scratch? We've done it repeatedly, so it's fixed scope. The audit gives your team the measurement either way.
A read-only replica or masked snapshot, your choice. No write access, no production, NDA first. Some clients run the eval suite themselves and share only outputs.
Then $2,500 bought you proof your agent is trustworthy, a claim almost no company can make. You keep the suite to re-verify whenever models or definitions change.
Postgres, ClickHouse, MySQL, Snowflake, BigQuery, Databricks, plus the agents on top of them. Foundations run on open-source dbt and Cube.
The alternative is renting your own definitions back from a vendor. Open source means zero fees, no lock-in, and a stack you keep forever.
Find out what your agent actually scores.
One week. Thirty questions. A number instead of a hope.