Applied AI Engineering
Test Before You Invest. Then Build It Right.
We’re an AI dev shop. We test your workload against real models, build the system your organization actually needs, and stay responsible for it in production.
Browse our proof library for the benchmark and implementation examples.
Four-time Microsoft Solutions Partner: Security, Data & AI, Azure Infrastructure, and Digital & App Innovation. Founder-led engineering experience since 1999.
Test before you invest, with a decision at each stage
Applied AI Engineering is Bonelli’s build-and-operate service line, with responsibilities agreed for each engagement. Start with a workload and its data boundary, test candidate approaches, and agree what evidence would justify a build. If you still need to choose a use case or plan adoption, start with Dallas AI consulting.
Separate evaluation, pilot, and production scope
- Evaluation: use representative tasks to compare model behavior, serving requirements, and limitations. Define acceptance criteria with the business owner; an impressive demo is not the acceptance test.
- Bounded pilot: agree the allowed data, users, integrations, failure cases, and human review before widening access.
- Build and secure: scope ingestion, inference, retrieval, application integration, access controls, and logging around the selected workflow.
- Operate: agree which co-managed or fully managed responsibilities the engagement includes, such as monitoring, change approval, escalation, and cost oversight. Name the production owner before handoff.
Explore model evaluation, private AI deployment, or AI product engineering for the relevant scope.
Inspect Bonelli’s own work
The Open LLM Test Bench is Bonelli’s own research, not an independent endorsement. Review its coverage and limitations rather than copying a model’s current rank into your purchasing decision.
For implemented work, read the anonymized local legal-evidence case brief and the AppellateNews build-and-operate case brief. You can also inspect 365 Security Assessment, Bonelli’s Microsoft 365 and Azure security-posture product. These are examples of Bonelli’s work, not promises of your project’s results.
Do we have to commit to a production build?
Define the evaluation and pilot separately from the build. Use the evidence to decide whether to proceed, change the design, or stop. Discuss scope and pricing for the actual engagement.
Can this start with a sensitive legal workflow?
Yes. The private-law-AI path starts with matter permissions, approved data, and human review. Schedule a strategy discussion with a workflow description, not confidential source files.
The problem
You are expected to accelerate AI adoption and protect the organization at the same time. Most providers recommend a model before they have tested one, hand over an architecture, and leave your team to carry the risk when it does not hold up.
Your workload determines the model. The evidence comes before the investment.
How the engagement works
The four pillars
Model Evaluation
Workload-specific model, runtime, quantization, serving, and hardware recommendations based on measured evidence.
Private AI
AI systems that run inside your control boundary when privileged, regulated, or proprietary data cannot be sent to a public AI service.
Autonomous Operations AI
Continuous monitoring and anomaly detection for operational telemetry, with escalation design, human override, and audit trails treated as first-class requirements.
AI Product Engineering
End-to-end AI product and platform engineering: ingestion, enrichment, retrieval, UI, APIs, cost controls, and operating discipline.
We publish our own homework — including the parts that did not work.
Bonelli Systems built and publishes a benchmark of locally servable open-weight LLMs. The point is not a model leaderboard. The point is the discipline: a controlled roster, eleven evaluation batteries, version-pinned judges where judgment is required, confidence intervals, statistical ties called out as ties, hardware provenance, and a published gap register.
Proof assets
Private AI legal evidence
A fully local evidence-processing pattern for privileged legal material: transcription, speaker diarization, document/image extraction, and search inside the client boundary.
Industrial telemetry monitoring
A public-safe case brief for energy and industrial environments where monitoring, escalation, and auditability matter as much as the model.
AppellateNews.com
A commercial legal research and analytics product built and operated end to end, with deterministic statistics and labeled AI enrichment.
Ownership continuum
Co-managed, fully managed, or build-and-operate: you choose how much Bonelli owns in production. We do not change the engineering standard based on the answer.
Related Bonelli capabilities
Next step
Turn the risk, AI, and Microsoft 365 conversation into a concrete operating plan.
Bonelli Systems helps regulated and growth-minded teams decide what to secure, what to automate, and what evidence to keep before another tool or AI workflow becomes business-critical.