Research first
We run new models early and test them on your documents, calls, and records before we recommend one. How we test
How we make AI hold up in production.
We test models on samples of your real work before we build, design the points where a person reviews or approves, and monitor quality, cost, and uptime once the system is live. Most of that is ordinary software, and we have been building it since 2007.
A demo is the easy part.
A useful pilot needs more than a working demo. It needs tests drawn from your work, a plan for errors, and someone responsible for monitoring it. We plan for all three from the start.
People in the loop
A person reviews, approves, or takes over wherever a mistake would be costly, inside the software your staff already use. Where people step in
Watched in production
Every model call, the cost, the answer quality, and the hardware underneath, with alerts that reach whoever is on call. What we watch
We learn how a model fails before you depend on it.
A production system is only as predictable as the testing behind it. We pick up new models early, so by the time we recommend one we have already seen where it breaks and how often.
- Open-weight language models since early 2023, in the months after Meta released LLaMA
- Small models such as Laya for choices, scores, and yes-or-no answers
- Segmentation and pose estimation for images and video
- Classifiers using Accord.NET since around 2010, and ML.NET after it
- Open-weight embedding models already shipping on-device in PDF Diff and Audio Search
Then we test on your work
- A test set drawn from your own documents, calls, or records, not only public benchmarks
- Candidate models compared on accuracy, speed, and cost for your task
- A written list of how each one fails, and how often
- License review, and hardware sizing on NVIDIA and AMD, before anything is bought
- A recommendation, including when AI is the wrong answer
The test set doesn't retire after the pilot. We rerun it before every model or prompt change in production.
We are not a partner of any chip, hardware, or cloud vendor, so the recommendation follows the test results.
People keep the final say.
We decide with you where a person reviews, approves, or takes over, then build those steps into the software your staff already use. Each deployment gets the ones its risk calls for.
Handoff to a person
The AI steps aside when someone asks for a person, or when a request falls outside what it should handle.
Approval before action
Anything that changes a record, sends a message, or moves money waits for a named person to approve it.
Review when unsure
Low-confidence results go to a review queue instead of straight through. You set the threshold and adjust it as the numbers come in.
Review screens
Staff see the source next to the model's answer and correct it in place, in the web, desktop, or mobile app they already use.
Corrections that count
Every correction is saved. Corrections become new test cases, and training data where retraining pays off.
An off switch
Staff can override any answer, and an administrator can turn a feature off and fall back to the manual process at any time.
The same steps apply when the AI can take actions. See how we build AI agents with approval before anything consequential.
Watched every day.
Models get updated, data drifts, and hardware wears out. We set up the monitoring a deployment needs and agree with you on who responds when it alerts.
| What we watch | What it catches |
|---|---|
| Every model call | Traces, metrics, and logs through OpenTelemetry show which model answered, how long it took, and where a request failed. |
| Cost and usage | Spend by team, model, and workflow, so a runaway prompt shows up on a dashboard before it shows up on an invoice. |
| Answer quality | Your test set rerun on a schedule and before every change, plus accuracy measured against staff corrections, so drift shows up as a number. |
| Uptime and speed | Alerts when a service fails or slows down, sent to whoever is on call, whether that is your team or ours. |
| Hardware | GPU memory, temperature, power, and network health on the servers we build and run, so a failing part is caught early. |
| Audit trail | Who asked, which model and version answered, and who approved the result, kept where your compliance team can review it. |
The model is the small part.
Review screens, queues, sign-in, audit logs, dashboards, and the servers underneath are ordinary software. They decide whether AI holds up in daily use, and they are what we have built for nearly twenty years.
- Review screens and queues in Blazor, React, Angular, WPF, .NET MAUI, or Flutter, depending on where your staff work
- Integrations with CRM, ERP, document systems, accounting software, and Asterisk and FreePBX phone systems
- Audit and correction records in SQL Server, PostgreSQL, MongoDB, or Elasticsearch
- Deployment pipelines and tracing with Docker, .NET Aspire, OpenTelemetry, and Azure DevOps
- GPU servers, networks, and equipment sensors we build and monitor ourselves
The full list is under tools on the About page.
From the model to the server
We scope the model, review screens, audit logs, and infrastructure together so each part supports the same workflow.
Built into what you have
People review AI output in the systems they already use, not in another tab they have to remember to check.
Services that connect AI to your work.
Engagements can cover one practice or several. We scope the work around your existing systems and the decisions people need to make.
AI consulting and implementation
Test use cases on your real work, compare open and commercial models, and deploy with human review where a mistake would be costly.
Systems architecture
Plan deployment on your servers, in colocation, or in your Azure, AWS, or GCP account, with monitoring, cost controls, and the option to move.
Custom software
Connect models to your PBX, CRM, documents, identity, and business applications, including the tools AI agents call.
Hardware and infrastructure
Size and build GPU servers and networks, then set up virtualization, deployment pipelines, and hardware monitoring.
Data and document intelligence
Make filings, contracts, claims, and archives searchable with source references, local embeddings, and retrieval over your documents.
Security and governance
Build approval steps, access controls, and audit logs around your requirements. We don’t certify compliance; we build systems your compliance team can review.
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Discovery
Map the workflow, data boundaries, and constraints. Test models on samples of your work.
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Architecture
Choose deployment, identity, review gates, and monitoring before implementation.
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Deploy
Integrate on infrastructure you control, with tracing and alerts in place.
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Optimize
Rerun tests before changes, review staff corrections, and monitor the system in use.
After discovery you get an honest estimate for your scope. We do not certify compliance.
What ships today, and what is next.
The patterns above are built for each engagement. Here is where they stand in our own products.
| Product | Today | Next |
|---|---|---|
| PalmConnect (Chat411) Early release |
Transfers the caller to a person on request, then leaves the call. Operators can follow the live transcript and see active calls. | A person approves anything that changes a record (groundwork built). Saved transcripts and call summaries (planned). |
| WolfPackAI Alpha · pilots available |
OpenTelemetry traces, metrics, and logs for each service in the Aspire dashboard, with basic authentication. | Role-based access control and richer monitoring are next on the roadmap. |
Bring the workflow you need to trust.
We will tell you what we would test first, where a person should stay involved, and what we would watch once it is live.