Connect a database today. Pipelines in production in days, not months.
Nectorq reads the shape of your data, builds and runs the pipelines, and recommends the KPIs. Within days, anyone in the company can ask it questions without writing SQL. It only ever sees column names and types. Your rows stay where they are.
Data engineering · AI FinOps · AI Security
Source
Pipeline
Ask
select sum(paid_amount) / sum(premium) from claims join policies where quarter = 'Q3'
In every deployment
- Only the schema reaches a model
- 20+ source connectors
- Cost of every call, by team
- Budget ceilings that stop things
- Role-based access per dataset
- Watched from the first run
- Air-gapped option
Point it at a database. Here is what comes back.
Four steps, and the first one is the only one you do by hand.
Connect
Give it a Postgres, Snowflake, S3 bucket, Salesforce org, or any of twenty-plus sources. It reads the tables, columns, types, and relationships. It does not read the data.
Build
Agents write the transformation logic, add quality checks, and deploy the pipeline to your warehouse. Then they look at the schema and suggest the KPIs a business like yours usually tracks. You edit the list.
Ask
Anyone types a question. “What is our loss ratio this quarter?” They get the number, where it came from, and the query behind it if they want it.
Watch
Monitoring attaches the moment the pipeline deploys. It knows when the 2am run did not happen, marks output stale, and flags a column that changed at the source.
A person confirms each step before the next one starts. Nothing goes to production because an agent decided it should.
The work your data team is three weeks behind on.
Most of a data engineer's week goes to plumbing that looks the same everywhere. This is that plumbing, done for you, checked, and left in your warehouse where you can read it.
The question gets answered, not ticketed.
Executives, analysts, and ops teams ask in their own words and get an answer from the governed tables, with the source named. The second time anyone asks something similar, it comes from memory, not from a model, so it is faster and costs nothing.
The platform that built it keeps watching it.
Monitoring attaches the moment a pipeline deploys. It knows when the 2am run did not happen, marks output stale before anyone opens the dashboard, and notices a column that changed at the source. Each incident names the stage and the change. Nothing is written to your data.
Root-cause analysis
When a run fails, an agent reads the brief, the pipeline, and the run history, then tells you what broke, why, which KPIs downstream are affected, and what you can do about it. It explains. It does not act.
Last run · 02:00
Incident
fct_settlements failed at build. A column changed in raw_payments.
Root cause
Why did this fail, what does it affect, and what can I do about it?
You will know what every answer cost, and who asked.
Every model call is priced and attributed as it happens: by team, by project, by person, by model. Set a monthly ceiling for a business unit and it holds. When it is reached, calls stop and the budget owner is told. Repeat questions are served from the semantic cache, so the bill falls as people use it more.
$8,420
spend · 4 teams
61%
from cache · no model call
1
ceiling reached · calls stopped
- Cost by team
- This month, last month, and the trend, down to the individual request.
- Ceilings that hold
- Organisation, business unit, team, project, or person. They nest and roll up.
- Cache savings
- How many questions never reached a model, and what that saved.
Any team can route its own model calls through the same gateway. Then the cost view covers all of your AI spend, not only the pipelines we build.
See AI FinOpsIt reads column names. It never reads a row.
The only thing sent to a language model is the schema: table names, column names, types, and how they join. Personal data and card data are filtered at the boundary before a request exists. Every user sees only the datasets their role allows. Every access, allow, and deny is written down with a reason.
- Schema only
- No sample rows, no values, ever.
- Filtered at source
- PII and PCI blocked before a request is formed.
- Access by role
- Every dataset and dashboard, scoped to the person asking.
- Your perimeter
- Shared, dedicated, or air-gapped with self-hosted models.
Built for data teams that need to ship faster.
Insurance, finance, and healthcare first, because those are the buyers who cannot send rows to a model and cannot afford a quarter per pipeline. The team keeps every pipeline it ships. It just ships more of them.
Suggestions, not a template — every KPI is yours to keep, edit, or replace.
Start with a pilot on one source. Grow from there.
Three tiers, priced by scale and deployment. Monitoring, AI FinOps, and AI Security are on every one of them. Tell us about the pilot and we will size it.
Pilot
Full access for 15–25 days at a reduced rate.
For a team that wants to prove the platform on one real project before committing.
Reduced rate · 15 to 25 days
- One real source, connected and modelled
- 15 to 25 day access window
- Sources and pipelines by custom scope
- Monitoring, AI FinOps, and AI Security included
Professional
For teams running these pipelines in production.
For a team past evaluation, with pipelines and analytics people depend on daily.
Annual · pricing on request
- Data engineering and Converse, scoped
- Dedicated deployment included
- Monitoring, AI FinOps, and audit trail
- Contracted source and pipeline capacity
Enterprise
recommendedOrg-wide scale, air-gapped, fully attributed.
For an organization putting every business unit's data and AI spend on one platform.
Custom · pricing on request
- Scoped by selection — price follows scope
- Air-gapped and dedicated deployment
- Contracted capacity, metered beyond
- SSO/SAML and priority support
Bring one database. Leave with a pipeline.
A pilot runs on one real source of yours, on an isolated slice or air-gapped if you need it. You keep everything that gets built.