Applied AI tools
One task, one tool with a clear input and result: a document in your company template, a risk review, an answer from your knowledge base.
As we maythink
AI adoption for organizations
AI that pays for itself on a real task.
We find the processes where AI delivers measurable results, build tools and multi-agent systems for them inside your perimeter, and bring them into everyday work — measured before and after.
- Task
- Baseline
- Pilot
- Rollout
- Impact
Access to a model is not adoption.
A company rolls out a chat and buys licenses. Three months later a handful of enthusiasts use it — and nobody can say what it has earned.
- There is a chat but no use cases: everyone invents their own reason to use AI.
- Good solutions stay personal tricks and never become a team tool.
- Pilots never reach production: no perimeter, no data access, no owner.
- The impact is never measured — so the next budget is impossible to defend.
We start with a task that has a price — not with a model.
Six steps from a task to proven impact.
Every solution goes through the same path. The metric and the success threshold are fixed before we build — so a pilot ends with a number, not an impression.
- 01
Find the tasks
We map processes and look for repetitive work that eats specialists’ hours.
- 02
Measure the baseline
How much time, money and errors the task costs today.
- 03
Build the solution
A tool, an agent or a multi-agent system on your own data and systems.
- 04
Run a pilot
With a real team, against a metric and a threshold agreed in advance.
- 05
Roll it out
Inside your perimeter: access, security, training, support.
- 06
Prove the impact
Compare with the baseline and decide: scale, improve or stop.
↻ Impact proven — on to the next task
Usually
- A chat subscription
- A prompting course
- A pilot for its own sake
- Impact “by feel”
With think
- A catalogue of working use cases
- A tool built for the task
- A pilot with a success threshold
- Impact in hours and money
We build what the task needs — nothing more.
Multi-agent systems
A complex process is split between agents: one gathers data from your systems and knowledge bases, another checks it, a third prepares the result for a person.
Your company’s AI perimeter
A single entry point: models, access keys for services, a tool catalogue, rules and a usage log — all inside your perimeter.
A showcase and an author’s path
Employees see what already works, vote for ideas and build tools themselves. Adoption grows from within instead of being pushed from the top.
An AI tool showcase for employees.
Not a chat but a catalogue: employees see where AI already helps in their work, who is behind each tool and what comes next. The hardest scenarios run as multi-agent systems, and ideas for new ones come from the employees themselves.
Multi-agent system
Multi-agent document preparation
One agent gathers requirements from the tracker, wiki and repositories, a second lays them out in the company template, a third checks completeness and contradictions. A person gets a draft and signs off.
Risk review
Takes apart a contract, a project or a codebase and shows the weak spots and what to fix first.
In development
Team working memory
Collects tasks, decisions and agreements from meetings, chats and files — and turns them into clear next steps.
Model access for your own services
Teams plug AI into their systems with the same sign-in as the chat — inside the perimeter, with a usage log.
Path of an idea
- Employee idea
- Colleagues vote
- An author takes it on
- Beta
- In production
- 4
- tools already in daily use
- 2
- in development, requested by teams
- 8
- employee ideas in the queue
Impact is the difference from the baseline, not a rollout report.
Before we build, we fix the baseline and the success threshold. After the pilot we compare the same metrics on the same task.
- Baseline
- Time, cost and quality of the task today — measured, not estimated.
- Metric
- One headline number the process owner understands.
- Threshold
- The value below which we do not scale the solution.
- Period
- The pilot length, after which the decision is made.
- Before, per task
- 4 h
- After, per task
- 1 h
- Tasks per month
- 30
- Cost of a specialist hour
- $60
- Impact per month
- 90 h · $5,400
The success threshold is set before the pilot: −40% time per task. If the pilot misses it, we do not scale.
AI works inside your perimeter.
- Models
- Your own models on-premise or private APIs — chosen to fit your data requirements.
- Sign-in
- Through corporate SSO, with roles and access rights.
- Log
- Who used which tool, and with what data.
- Rules
- Your security team decides what data may go to a model — we build those rules into the tools.
Get started
Don’t adopt AI “in general”.
Start with a specific task.
- Bring the process that takes up the most hours.
- In 2–3 weeks we show a measured baseline and a working prototype.
- The decision to scale rests on the pilot’s numbers.
Or write to us: info@aswemay.ru
Think first. Then automate.