Agents Platform
ProductConfigure, deploy and operate AI agents in production, with the integration and observability you need before trusting them with something that matters.
Status
In production with clients
Persiscal · Product factory
We turn your problem into an AI-Native business.
We find the real problem, test it with real users before building anything, and only then accelerate the engineering. If there is no business in it, we tell you.
Problem → Business
- 01Problem
- 02Experiment
- 03Validation
- 04Product
- 05Business
The usual scene
A product nobody asked for.
Someone handed over the requirements. People, code and roadmap piled on. Six months later something shipped that works perfectly and almost nobody uses. Engineering did not fail: nobody checked, before building, that the problem was worth solving.
The usual path
The requirements arrive
More people, more code, more roadmap
A product nobody asked for
The path with evidence
A problem that genuinely hurts
Experiments with real users
A product with proven demand
The bottleneck is not engineering. It is knowing what deserves to be built.
Your team knows how to build. We accelerate that engineering — but only once the evidence says there is something there. That is the whole difference between a product and a product someone buys.
What decides whether there is a business
- The problem exists and it hurts
- Someone owns that problem
- That someone holds a budget
- The solution beats the current process
- Those customers can actually be reached
- AI genuinely changes the economics
How we work
Five stages. Nothing advances without evidence.
The same route for every initiative, yours or ours. Each stage has an explicit output: no output, no next stage.
Find the problem worth solving.
We start from your operation, your customers and your numbers — not from a feature list. We come out with a written hypothesis: who suffers what, what it costs them, and why now.
Your operation and your dataInterviews with whoever suffers the problemWhat was tried before and why it failedWhat AI makes possible that was not
Output
Problem hypothesis
Saying no is also delivering value.
A two-week experiment that kills a bad idea is worth more than a flawless product nobody buys. You keep the answer, the learning, and the budget you did not spend.
The proof
We ran it on our own money first.
The same route we are proposing to you, we ran on our own products. Here they are, each at a different stage of the cycle.
Second Brain
ExperimentA reasoning layer that represents how your company thinks: teams ask, test ideas against organizational knowledge, and generate new insight instead of rediscovering what the company already knew.
Status
Private beta · looking for 3 pilot companies
Thesis Evaluator
LabSeveral agents evaluate an academic thesis from separate perspectives — architecture, product, engineering and writing — then synthesize it all into actionable feedback.
Status
Live experiment
Where to start
Three ways to work together.
Each one starts differently and ends with something different in your hands. The first exists so you never have to bet before you know.
Consulting is not the opposite of this. It is where the problems come from.
Client work is where we meet real problems, real constraints and real buyers. It funds the experiments and points them at what matters.
Revenue engineMarket researchProblem discoveryDistribution channelR&D funding
“We don't just build software for our clients. We discover how technology can create new businesses with them.”
Four ways the economics can work
Client-owned product
We build an AI-Native product for you. You own it. We charge for discovery, development and maintenance.
Persiscal-owned SaaS
We identify the opportunity and operate it. Revenue comes from subscription, usage and enterprise licensing.
Joint venture
We contribute product and technology capability; you contribute domain knowledge, customers or distribution. Equity, revenue share or licensing.
Spin-off
A product outgrows the portfolio and becomes its own company. Persiscal stays on as shareholder, technology partner or incubator.
How we think about the portfolio
Not every experiment deserves to become a company.
Building cheaply carries one danger: building too much. So every initiative, yours or ours, knows which bucket it is in and what it has to prove to move up.
Labs
Learn.
Measured byDid we learn something strategically useful? These do not need revenue.Purpose: Learn.Experiments
Validate a business hypothesis.
Measured byActive users, interviews, pilots, conversion, willingness to pay.Purpose: Validate a business hypothesis.Products
Generate recurring revenue.
Measured byMRR, retention, acquisition cost, gross margin, growth.Purpose: Generate recurring revenue.Strategic bet
Become a significant business.
Measured byDisproportionate investment, once the evidence justifies it.Purpose: Become a significant business.
Illustrative ratios, not targets. The point is the shape of the funnel, not the numbers.
How we decide where the investment goes
Every initiative is scored on the same eight dimensions and reviewed once a month. That review produces exactly one decision. It is why zombie projects do not survive here.
Scored on
Problem severityMarket sizeWillingness to payCompetitive advantageDistributionAI leverageEvidenceStrategic learning
One decision a month
KillPauseContinueProductizeScale
The engineering behind it
Without this, none of the above would be credible.
These capabilities hold the method up. They are not the positioning — they are why we can promise weeks instead of quarters.
Problem discovery
Rapid experimentation
AI engineering
Engineering acceleration
Product strategy
SaaS infrastructure
Agent systems
Legacy modernization
What we believe
We are not a software factory. We are a product factory.
Your team knows how to build. The hard part is knowing which problem is worth building for, and proving it before a roadmap is committed to it.
The next generation of technology companies will not win because they write software faster. They will win because they learn faster.
Some experiments will fail. Some will become products. A few will become businesses.
Every experiment makes us better at the next one.
Experiments are cheap. Products are expensive. Businesses are extremely expensive. So we spend the cheap thing to find out about the expensive ones.
We are not trying to become a company with many products. We are trying to be the partner that turns a business problem into an AI-Native business, over and over again.
Bring us a problem worth solving.
Ready to transform?
Not a requirements document. A problem.
Tell us what is expensive, broken or impossible in your business. We will tell you whether there is an AI-Native business in it, and what the smallest experiment to find out looks like.