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 (cart abandonment, 2026); IHL Group (inventory distortion, 2025). Both are industry-wide figures, not what Sriya.AI recovers.
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
Our scores were ahead of ChatGPT and Gemini in 6 of 6 comparisons, and ahead of standard ML models in 14 of 15 further tests. These are our own tests on public data sets, not independently audited. LLM results are as reported in our case study (ChatGPT-5.2 and Gemini-3).
- 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
Score 20 → 30Conversion 2% → 3%
SUPPLY · SXI
Risk 25 → 15Stock-outs 6% → 4%
Illustrative Example for a $100M retailer: $1.2M a year, payback in about 9 months. Illustrative values on placeholder inputs, not customer results.
Live product
Demand and supply, fixed together.
$830K
a year: recovered sales plus lower buffer costs
$2.71M
net annual cost of inaction
2 of 10
solutions running together: demand and supply
Sample data From our Executive Decision Intelligence Dashboard on sample data. These are targets, not measured customer results. 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, 2 US IPOs, 25 patents. Funded by Kleiner Perkins, 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. Business & Technology Transformation Leader at LTM (formerly LTIMindtree). Delivered AI, data science and ML solutions across industries.
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.