Basantra Systems
Proof before production.
Greek assayers trusted the streak on the touchstone over the stamp on the coin. We hold AI to the same test: products, consulting and training built around evidence your auditor can check.
Where AI programs stall
- Outputs drift between releases.
- Agents take actions with no record of why.
- Finance asks for a number and the model can't defend it.
Ways to work with us
Products
Software that checks agents before they act, tests models on every change, and keeps numbers traceable to their source.
AI Offerings
Fixed scope, fixed price, defined deliverable: an assessment, a production agent, or a model evaluation.
Consulting
Architecture and strategy for data, AI and cloud platforms, led by a senior practitioner from scoping to handover.
Training
Briefings, workshops and programs for the people who build AI systems and the people who sign off on them.
Why Basantra
The name comes from basanos, the touchstone Greek assayers used to test gold. Draw a coin across the stone and the streak shows what the metal is, whatever the stamp claims.
Most AI programs stall between pilot and production because nobody can prove the system does what the demo promised. Basantra builds, advises and teaches with one rule: show the evidence.
About BasantraHow an engagement starts
A 30-minute call
You describe the system and the decision riding on it. We tell you plainly whether we can help.
A written scope
Deliverables, dates and responsibilities on paper, with a fixed price where the work allows it.
A first deliverable
Something you can inspect within 2 to 3 weeks of the start date.
Recent insights
Spark Declarative Pipelines: What You Own After You Adopt Them
An evaluation of Apache Spark 4.2.0 Declarative Pipelines, measured on a single-node OCI lab, checked against vendor documentation, with the architecture consequences for teams already running Airflow and dbt.
The Use-Case Lottery
Enterprise AI portfolios fail at selection, not execution. Most are stacks of lottery tickets bought by whoever pitched loudest, and the fix is portfolio governance, not more pilots.
From Projection to Proof: A Framework for Measuring AI Productivity Gains
Calling the productivity measurement problem a Luddite argument misses the point. AI can deliver real gains. Most organisations are measuring for the wrong trajectory and getting false negatives as a result. Here is what rigorous looks like.