Antares trains and certifies every employee, then installs an AI operating system (a Brain, an Advisor, and 35 AI Employees) that already knows your business and runs the workflows they build.
Menders runs 15 locations across two countries. 50 therapists, six departments, four operating tools before Antares. They tried hiring an ops manager. They tried bolting on more SaaS. Then they put the business on Antares.
Connected Slack, Google Workspace, Microsoft 365, and Granola. The Brain began indexing six months of operating history. The first morning brief landed on day seven.
1,400 entities in the Brain. Eight AI employees running daily across operations, finance, and intake. The weekly leadership report writes itself.
Twelve AI employees active. The operations lead surfaces location-level anomalies before the weekly call. The Brain answers most questions the team used to bring to the founder.
Antares runs the standing slate across all 15 locations. The team scaled into four new cities without hiring an ops manager.
Antares is the only system that holds the picture in one place. My team stopped writing weekly reports because the report writes itself.
Antares OS does the work. Antares Coach is how a company learns to use it. Together they take a company from “we bought some AI tools” to “every department has AI that knows our actual work.”
Reads a company's documents, SOPs, and workflows, builds a private knowledge graph from them, and runs AI employees that work from that knowledge.
Structured training and certification that gets a company's people actually using the platform, with a clear path and proof of progress.
A company does not sign up and stare at a blank setup screen. Our team installs the system through a guided engagement, then stays for the part most AI projects skip: making it stick.
Our team works directly with employees on their real tasks, so people learn AI in the context of their own job rather than in the abstract. While the training runs, we gather the company's SOPs, templates, and workflows. That is the raw material the platform learns from, collected as a natural part of teaching.
The training period is also when the system learns the company.When training ends, Antares OS + Coach stays installed. The AI employees are already populated from everything we gathered, and certified staff already have access. The agents speak the company's language on day one because they were built from the company's own documents during the weeks of training.
No cold start. The work carries straight over from training into the product.We do not hand over a login and leave. A product champion keeps employees improving, finds new tasks worth automating, and reports to leadership on what is working. As the company grows, we add agents, write new training modules, and connect the systems the company already runs on.
Services after launch: new agents, new training, deeper integration.The engagement moves from trust, to practice, to proof, to OS translation. Each week has a live training job and a Coach artifact the company keeps.
Survey employees, interview leadership, define sensitive-data boundaries, and segment training groups.
Our trainers teach AI basics, safe use, prompting, output review, and company-specific examples in person. Coach assigns each employee the relevant refreshers.
Employees identify recurring tasks, handoffs, documents, and decisions. Coach uses their profile to turn those into candidate AI workflows.
Employees complete one profile-matched real task with AI, document the review step, and submit evidence. Trainers approve, edit, or reject.
The best workflows become playbooks. Repeated patterns update the operating profile. Gaps become the build backlog inside Antares OS.
Ten modules, each tied back to your company's work. Employees can start from zero — many people have never used AI at work, and the course meets them there without embarrassment. Completion is not the bar: every module ends in evidence a trainer reviews, and certification is what unlocks an employee's agents.
| Module | What they learn | Practice task | Proof of skill |
|---|---|---|---|
| 01What AI is good for | Where AI helps, where it does not, with examples chosen for the employee's role. | Classify 10 tasks from their own work by AI fit. | Names 3 strong uses and 3 limits for their role. |
| 02Rules of the road | What can and cannot go into AI tools, based on the employee's data risk. | Mark role-relevant inputs as allowed, redacted, or forbidden. | Passes the safety boundary check for their role. |
| 03Prompting basics | Instructions, context, examples, constraints, and output format. | Rewrite a vague prompt into a usable one. | Prompt includes context, task, constraints, and format. |
| 04Reviewing AI output | Spotting hallucinations, missing context, stale facts, and overconfidence. | Find the flaws in 3 AI-generated answers. | Identifies errors and states how to verify them. |
| 05Everyday workflows | Summarize, draft, compare, extract, plan, transform, filtered to the role. | Pick 2 workflows relevant to their profile. | Selected workflows saved to their profile. |
| 06Company context | Using SOPs and templates to improve output without leaking sensitive data. | Turn one process into reusable AI context. | Creates a sanitized workflow context block. |
| 07Use-case builder | Moving from generic AI to actual work, prefilled from their profile. | Identify 3 weekly tasks AI can improve. | Three candidate use cases submitted. |
| 08Workflow lab | Converting a use case into a repeatable workflow shaped by role and risk. | Build one AI-assisted workflow end to end. | Workflow has input, prompt, review, output, risk notes. |
| 09Review and escalation | When to trust, verify, escalate, or avoid AI. | Choose the right path for mixed-risk scenarios. | Handles high-risk cases correctly. |
| 10Practical certification | Proving safe, useful application on a real task. | Complete one real-work task with review and escalation notes. | Certified when output is useful, safe, reviewed, explainable. |
Before a single lesson, every employee gets an AI profile: their role, confidence, tools, recurring tasks, data risk, and the use cases they are cleared for. Modules adapt their examples, practice, and safety scenarios to that profile, and the same profile configures the agents they unlock.
That is the difference between a course someone forgets and a workflow they use the next morning.
Because Antares runs the agents, the manager view reports readiness and real impact: who is ready, who is risky, who is blocked, and which workflows are worth building for the whole company.
Most AI is frozen at the moment it gets set up. Antares OS runs a continuous ingestion heartbeat: around the clock it pulls in new documents, updated SOPs, and fresh activity, then folds them into the knowledge graph. The agents work from how the company runs today, not from a snapshot taken on the first day.
AI training programs are good at teaching people to use AI. We do that too — and then we leave behind a product that does the work. Select any row to go deeper.
Both teach. The difference is what is left running afterward: certified people on the one hand, an operating system doing the work on the other.
Habits fade and people leave. Agents grounded in the company's documents stay, and they keep the knowledge even as staff turn over.
Generic tools start from zero every time. Ours start already knowing the company, because the agents were built during the training weeks.
The company keeps its raw inputs. The knowledge the system derives from them lives in our platform and grows with use, which is what makes the product better over time.
For training-only programs, the certificate is the finish line. For us it is a key: passing certification is what gives an employee access to their department's agents.
Certification rate shows people took the course. Because we run the agents, we can report the work they actually did, by department, which is the number leadership cares about.
A finished course stays finished. Workflows your team approves feed back into the Advisor, so the platform fits each role better the longer the company runs on it.
Answers cite the documents they used, and every model call is recorded with its cost. That kind of evidence does two jobs: it gives a security team something concrete to review, and it makes the next answer better.
Security-review pack (DPA, subprocessor list) in progress for the 2026 cohort; SOC 2 path planned.
Ninety days free, with the training engagement and dedicated onboarding from the team that built it. After that you pay per seat at standard pricing. If it has not earned its keep by day ninety, you keep your Brain export and we part ways.
A course ends at the certificate. Antares uses the training to build something the company keeps: AI employees, grounded in your own documents, that do real work after the trainers leave. Certification is not the finish line, it is the key that unlocks each person's agents.
Both, on purpose. The course builds on the best AI education available rather than reinventing it, and the Antares layer is what makes it stick: every lesson is company-specific, role-aware, and evidence-based. Generic concepts become real-work exercises on your own SOPs, reviewed by a trainer.
Company-wide, by design, but delivered department by department so each group learns on its own real tasks. Every employee gets a personalized path based on their role, tools, and data risk.
Passing the practical assessment for a role gives that employee access to their department's company-data agents, enforced on the server. Trust-sensitive workflows never unlock by clicking through lessons. Early cohorts are graded by a trainer, not auto-scored.
Your data lives in your isolated tenant and never trains a third-party model. Each company is strictly separated from every other. The knowledge graph the system derives from your documents stays private to your company, always.
Six to eight weeks from baseline to OS translation. Six if your processes are already documented, eight if they are messy. The AI employees are populated during those weeks, so there is no cold start when training ends.
Yes. BYOK is supported, so AI requests route through your own provider key with zero-retention settings, or you can use ours behind the per-seat fee.
You export everything. Request a full export of your Brain and we provide it, and we delete your tenant within 30 days. There is no lock-in clause in the contract.
We train the people, hand over a product that already knows the business, and stay to help it grow. The company trains once, and the software keeps returning the favor.