Find
Data Scientist · steps 1–2
Finds who will buy, and checks what you can ship.
The need
Demand leaks. Supply misfires.
~70%
of visitors leave before buying
$1.7T
lost a year to stock-outs and overstock
2
systems that never talk: the site and the stockroom
Nobody connects them. We do.
Sources: Baymard Institute (2026); IHL Group (2025).
The solution
Three agents. One job: sell what you can ship.
Explain
AI-ML Expert · steps 3–4
Says why, with every number cited.
Act
MLOps Engineer · steps 5–11
Plans, assigns, runs and checks the result.
1234567891011
Agent workExplanationPeople approve (steps 5 and 11)
People approve twice.Autonomy is earned task by task, not assumed.
On-prem, on CPU, not GPU.Customer data never leaves. Only model results go to the cloud for reasoning.
Proof
We have shipped before. And our scores beat ChatGPT and Gemini.
Backorders: true cases caught
Ahead of ChatGPT and Gemini in 6 of 6 comparisons, and ahead of standard ML models in 14 of 15 further tests. Our own tests on public data sets.
- 20+paying customers on our earlier SaaS
- $25Kpeak monthly revenue
- 37modules, ready to deploy
- 3–5customers in talks today
Then: demand only, in the cloud. Now: demand and supply, on-prem. Earlier product results: 200% conversion lift (outdoor retail); 19% to 50% (aquarium).
Why it works
Move the score. Move the result.
Seven to nine models feed one fixed formula. Each score is tied to a business outcome, so improving the score improves the result.
Score
7–9 models, one fixed formula.
Correlate
Each score tied to an outcome.
Improve
Move the score, move the result.
DEMAND · DXI · EXAMPLE
Score 20 → 30Conversion 2% → 3%
SUPPLY · SXI · EXAMPLE
Risk 25 → 15Stock-outs 6% → 4%
Example for a $100M retailer: projected $1.2M a year, payback in about 9 months.
Live product
Demand and supply, fixed together.
$830K
projected a year: recovered sales plus lower buffer costs
$2.71M
projected net annual cost of inaction
2 of 10
solutions running together: demand and supply
Projected from our Executive Decision Intelligence Dashboard. We sync both sides, where most vendors cover one: they either personalize the site or plan the stock.
Team
Proven founders, plus an enterprise growth leader.
Srinivas Kilambi, Ph.D., C.F.A.
Founder & CEO
6x entrepreneur, 25 patents. CSO, Commodore Applied Technologies (IPO). Co-founder and CSO, Commodore Separation Technologies (IPO). Founded Sriya Innovations (Renmatix) and Sriya Green Materials (Verdecem), both Kleiner Perkins-funded. Also funded by BASF, Gates Foundation, Bright, Holcim.
Dr. Mahesh Banavar
R&D
Professor of Electrical Engineering, Clarkson U. Academic Dean.
Shashi Kidambi
Business Development
20+ years growing enterprise IT businesses. Led $5M+ enterprise software accounts from sale to service. Won multimillion-dollar accounts at a Fortune 100 logistics company. Business & Technology Transformation Leader at LTM (formerly LTIMindtree).
Prashant Yadav
VP Tech
M.S. Computer Science. 5 years with the team.
Our book, published by Springer
Large Numerical Models from a Business Perspective (Kilambi and Banavar)
Team of 8 in the US and India · 6 part-time advisors · 7 US provisional patents filed
Get in touch
Let's fix your demand and supply together.
Retailers and distributors: ask for a walkthrough. Investors: we are raising a $4M seed round.