Data collection
We map the data in the areas that matter — the ones that hurt, the ones that drive revenue, the ones nobody can audit end to end. In whatever format that data already exists today.
Solutions · Consulting
AI consulting built on the data you already have
Your company records everything: service conversations, résumés, calls, transactions, incidents. What is missing is not data — it is turning that material into something an AI can apply, and then reading in it what nobody has the time to read.
Projects run by Hal-AI Systems, part of the 2CW group.
Service dataset · Aurora Store
automated conversion — runs again on every new load
| Column | Source | Tokenization | Fields |
|---|---|---|---|
| service_transcript | Call center | Converted | 4,210 |
| contact_reason | Free-text CRM field | Converted | 4,210 |
| post_service_score | Form | Converting | 3,860 |
| ticket_attachment | Scanned PDF | Read queue | 1,900 |
| Fields converted in this cycle | 4,210 + 4,210 + 3,860 + 1,900 | 14,180 | |
The method
Four steps, from raw data to the pattern nobody had seen
The work does not start by buying technology. It starts by looking at what your operation already produces every day and turning that into training material.
Tokenization
That data is converted into a model the AI can apply. The process is automated: when new data comes in, the same conversion runs again, with no manual rework.
Training in the Hal-AI interface
The tokenized data is applied to the Hal-AI interface for training — scoring, sales maximization, customer satisfaction, or whatever the goal of the project turns out to be.
Patterns and opportunities
Hal-AI surfaces patterns and opportunities hidden in the operation: correlations that reading the same numbers by hand never reaches, simply because the volume is too large.
Once the four steps close, the project becomes a screen. The one at the top of this page is step 2, with the dataset being converted; this one is step 4, with what the reading found inside it.
Patterns found in the dataset
Reading over the Aurora Store service dataset — 4 tokenized columns, 12 weeks of records.
Dataset up to date
Repeat contact within 48h
1,892
Agent handoff mid-conversation
1,244
Contact with no reason recorded
612
Same-day callback after a long wait
448
Repeat contact within 48h, week by week
917 cases across the 6 weeks shown
Where the pattern meets the operation
the same tokenized dataset feeds the channel agent, the report and the HAL Copilot
What you are looking at
On the left, the four steps of the method, with Patterns open and the three earlier ones already completed on the same dataset; beside it, every pattern the reading found appears with the number of service conversations it repeats in, and the chart opens the main pattern up week by week.
The screen at the top of this page is the previous step: the columns of the dataset, where each one came from and how far along its conversion is.
The advantage
Nobody picked a sample or opened a spreadsheet: the pattern was read across the entire dataset and arrives named, counted and dated, with the week it started growing in plain sight.
Because tokenization is automated, the same reading runs again whenever new data comes in — and the result is born inside the platform the team already uses, not in a separate report.
Why tokenization is the step that decides the project
An exported spreadsheet, a scanned PDF, a call recording and a free-text field in a legacy system are not the same thing to a model. Tokenization is what puts all of it into a single, usable format.
Because the process is automated, the project does not die on delivery: the operation keeps feeding the same model month after month.
AI consulting is not a slide deck of recommendations. It is your own operational data — converted, trained and handed back inside the platform your team works in.
- The technical object is the same one behind the platform: agents with memory and context, the subject of the patent filed with the USPTO in 2025.
- The result lands in Hal-AI Agentic — agents, reports and the HAL Copilot.
- Your source systems stay exactly where they are, reached through APIs and MCP.
+35
years of experience across the 2CW group
2022
the year Hal-AI launched
USPTO
memory patent filed in 2025
Application
Hiring and HR
A single opening at a large company draws hundreds or thousands of résumés. Reading all of them never actually happens — in practice, someone reads the first few and the rest becomes a pile.
- Screening of hundreds or thousands of résumés per opening
- Classification in seconds, against the criteria HR defined
- The same criteria applied to every candidate, first to last
- Profile patterns that repeat among the people who thrived in the role
The model is trained on the company's own history: the roles it opens, the vocabulary of the industry, what the operation has already learned about who adapts to the job. The final call still belongs to the HR team — what changes is that it is now made across the entire pool.
Application
Automated service quality scoring
Quality auditing is almost always sampling: someone listens to a handful of calls each month and draws conclusions. Once conversations are tokenized, the review stops being a sample and starts covering everything.
Conversation scoring
Every conversation gets a score against the criteria your company set — not some generic industry benchmark.
Conversion patterns
What the conversations that closed a sale have in common, and what keeps repeating in the ones that stalled halfway.
Performance ranking
Agents compared on the same yardstick, with the full conversation available whenever someone wants to check.
Real-time alerts
When a conversation goes off the rails, the supervisor is notified while it is still happening.
In day-to-day operations this connects straight to the call center and to call auditing: the same reading that scores the history starts following what is in progress right now.
Application
Fraud detection and compliance
Fraud and process drift rarely show up in a single record. They show up in the pattern: the exception that keeps repeating, the deviation that always happens at the same point in the flow.
Fraud prevention and alerting
The model learns what normal behavior looks like in your operation and flags what falls outside it. Instead of finding out after the loss, the responsible team gets the alert the moment the anomalous pattern appears.
Compliance and process conformity
Verification that the process was followed exactly as written — step by step, across the full volume, rather than on a sample picked at the end of the quarter.
Loss management and financial optimization
Where money leaks out without anyone having decided it should: rework, shrinkage, a manual exception that quietly became the rule. The pattern shows up once the data is read together.
The access, logging and processing controls that apply to this data are described on the Security page.
And much more
The method stays the same; the area of interest is yours
HR, service quality and compliance are the most requested fronts. The path — collect, tokenize, train, read the patterns — does not change when the subject does.
- Scoring and performance review
- Sales maximization
- Customer satisfaction
- Hidden patterns in the operation
- Opportunities by customer segment
- End-to-end process quality
The area of interest is defined together with your team in the first conversation — starting from what the operation already records and what goes unread today for lack of time.
Questions
What people usually ask before getting started
Do we need a data lake in place before we start?
No. The first step of the method is collecting data across the areas you care about, in whatever format it already exists. Tokenization is precisely the stage that turns that material into a model the AI can apply.
What exactly is tokenization in the Hal-AI method?
It is the conversion of your operational data into a model the AI can work with. The process is automated, so the same conversion runs again whenever new data arrives — no redoing the work by hand every cycle.
Does the consulting project turn into something inside the platform?
Yes. The tokenized data is applied to the Hal-AI interface for training and starts feeding what the operation uses every day: the channel agents, the reports published by the agent and the HAL Copilot.
How are privacy and the LGPD handled in this kind of project?
The data remains the company's own. Processing follows the controls described on the Security page, including the LGPD, access profiles and a record of what was done inside the platform.
Can it connect to the systems we already run?
Yes. The platform talks to your systems over APIs and over MCP, as described in Integrations and API. Your ERP, your CRM and your ticketing system stay right where they are.
Book an assessment
Bring us the area that hurts most. Together we look at the data it already produces, what can be tokenized first and the kind of pattern that base is able to reveal.