The Real Problem
They record what happened. They do not tell you whether your rep actually understood the customer's problem — or just filled in the fields to pass inspection.
Who Feels This
When qualification lives in spreadsheets, summaries, and rep intuition — everyone is flying blind on deal quality.
The Problem
Most sales teams confuse what was discussed with what was confirmed. The result is a pipeline full of opinions disguised as facts.
The customer mentioned a problem. The rep marked it as identified. But the customer never actually said it was their problem, causing it, costing them something specific.
By the time an opportunity reaches a review, it carries whatever the rep believed about the conversation — not what the customer actually established.
Deals that looked qualified at Discovery stall at Technical Evaluation. SE time gets spent on opportunities that were never actually real.
About the Founder
I spent over 10 years closing enterprise deals — from 8K SaaS contracts to a 0.2M multi-year cybersecurity engagement. Across every company, the same problem showed up in every pipeline review: nobody actually knew which deals were real.
Reps advanced opportunities because the CRM fields were filled, not because the customer had confirmed a business problem. Managers reviewed forecasts built on self-reported confidence, not evidence. Note-takers captured what was said. Nothing captured whether it meant anything.
I sat in hundreds of discovery calls and watched this happen in real time. The rep would ask a question, the customer would give a vague answer, and the rep would write “Confirmed” in the CRM. No one challenged it. The deal moved forward. Quarters got missed.
I built Pramāṇa to fix that specific problem. After 10+ years in enterprise sales, I made a deliberate shift into technical learning — earning my SnowPro Core Certification, completing the DataCamp Associate Data Engineer certification, and earning five Snowflake hands-on badges. I wanted to build the solution myself, not just describe it to an engineer.
Pramāṇa is the result: an AI-powered evidence gate built on Snowflake, grounded in 10 years of watching what actually goes wrong in enterprise sales.
How It Works
Automatic ingestion. Custom qualification framework. Structured verdict — from first call to final decision.
Your note-taker — Gong, Fireflies, or similar — sends the call transcript directly to Pramāṇa. No manual work. No copy-paste. Every discovery call is evaluated without anyone remembering to submit it.
Before your first call is evaluated, we run a qualification design session with you. We learn the real problems your product solves, what confirmed evidence sounds like in your deals, and what separates a genuine business problem from a symptom. That framework — unique to your product and your market — is what every transcript is measured against. Not a generic scoring rubric.
You get an ADVANCE or DO NOT ADVANCE decision, with the exact evidence behind each dimension, the gaps, and the specific questions to ask next.
The Evidence Gate
Evidence from different business problems is never mixed. Each problem must earn its own verdict independently.
Did the customer explicitly confirm they have this problem — not just that they were asked about it?
Did the customer confirm a real business consequence — lost revenue, increased cost, reduced performance — tied to this specific problem?
Did the customer identify what is driving the problem — not a rep's hypothesis about what might be causing it?
Did the customer state or confirm a specific number — a dollar value, a percentage, a volume — tied to the impact?
What You Get
Every evaluation shows the status of each evidence dimension — Confirmed, Hypothesis, or Missing — with the exact customer statement behind each conclusion.
Who It's For
After each call, you get a specific list of questions — targeted at the exact evidence dimensions that are still missing — so the next conversation moves the deal forward, not sideways.
Every opportunity review shows the cumulative evidence state across all calls — what's confirmed, what's still hypothetical, and which deals are genuinely ready to advance.
Technical evaluations and POCs only start when the business case is real. The evidence gate ensures SE resources go to deals with confirmed problem-impact-quantification.
Pricing
No per-seat licences. No feature tiers. Usage-based pricing with a small monthly minimum — so light users pay less, and heavy users always pay fairly.
Every month includes a base number of evaluations. Go above that and you pay a small per-evaluation fee — nothing else.
Customers who join during early access lock in founding rates permanently — before standard pricing is published. Rates are confirmed during your onboarding session.
Data Security
Pramāṇa is built for teams who take data governance seriously. Every customer gets a dedicated, isolated data environment — and contractual guarantees to match.
Your transcripts, calls, and evaluation results are stored in an environment exclusive to your organisation — completely isolated from every other Pramāṇa customer. No shared tables. No shared access.
Strict role-based access controls mean your team sees only your data. It is architecturally impossible for one customer's data to be read, queried, or accessed by another.
Your discovery conversations are never used to train or fine-tune any AI model — by Pramāṇa or any underlying AI provider. Evaluation runs in inference mode only, on your data, for your team.
We act as your data processor. A DPA — covering data handling, retention, breach notification, and sub-processor disclosure — is provided to every customer before they go live. No need to ask your legal team to chase it.
Get Started
See Pramāṇa on a real transcript from your team. Bring a call. Leave with a verdict.