◇ AI · ML · 2026
AI that ships, gets measured, and earns its budget.
Tridence designs and engineers AI that lives inside real products and operations—LLM agents, predictive models, computer vision, and the unsexy plumbing that makes any of it actually work in production. We start with the use case, not the model.
Tailored · To your scope · No flat-rate menus
The honest part
AI is not a strategy. It is a tool.
Most "AI initiatives" stall in the gap between a polished demo and a system that runs at 2 a.m. on a Tuesday. We do not pitch transformations. We pick the use case where AI clearly beats the alternative, ship it with evals and guardrails, and measure the dollar it produced or saved.
Demo · Pilot · Stall
The notebook works on a clean dataset. The slide deck has a $40M number on it. Six months later, nothing is in production, the model has drifted, and the steering committee is naming itself.
This is the AI program your CFO will quietly cancel.
Use case · Ship · Measure
One narrow use case. Real data, real users, real evals. A clear baseline, a clear lift, and a runbook for the day the model misbehaves. Then the next use case, then the next.
This is the AI program that compounds.
What we ship
Six AI disciplines. One measured outcome.
Whether you need a generative-AI feature in a product, a forecasting model in operations, or a vision system on a line, the work fits one of these tracks.
Generative AI & LLM apps
Customer-facing copilots, internal assistants, document-grounded Q&A, and structured-output pipelines—built with retrieval, tools, and evals on by default, not a single-call prompt.
Agents & workflows
Multi-step agents that read, reason, act in your tools, and hand off to a human at the right moment. Scoped narrowly on purpose. We do not ship "do anything" bots.
Predictive ML & forecasting
Churn, demand, fraud, lead scoring, pricing, supply—classical ML done right beats hype every quarter. Feature stores, training pipelines, and drift monitors included.
Computer vision
Defect detection, OCR, document understanding, and edge inference. From annotation pipeline to deployed model to the dashboard the operations team actually uses.
Data & MLOps platform
The unsexy half: warehousing, feature stores, training pipelines, eval harnesses, model registries, and CI/CD for models. AI without this is a demo, not a system.
Evals, guardrails & AI governance
Regression suites, content filters, PII redaction, jailbreak monitoring, model cards, and audit logs. We design for the model misbehaving, because eventually it will.
How engagements start
Three entry points. One standard.
No SKUs, no flat-rate menus. Pick the front door that matches where you are; we size the work after a real conversation.
AI Use-Case Audit
- Use-case discovery & ranking
- Data & readiness teardown
- Risk, compliance, and cost map
- Prioritized 90-day build plan
AI Feature or Model Build
- Data, model, evals, guardrails
- Production integration & CI/CD
- Dashboards & cost monitoring
- Day-30 lift target baked in
Continuous AI Care
- Drift, eval & cost monitoring
- Prompt & model regression
- Quarterly model reviews
- New use cases on the roadmap
How the work moves
Frame. Build. Ship. Tend.
Every engagement runs on the same loop. The names are unglamorous on purpose.
Use case & baseline
The decision being made, the data on hand, the baseline being beaten, and the dollar at stake—named in writing before a model is touched.
Data, model, evals
Pipelines, training, prompts, and guardrails alongside an eval harness that fails the build before users do. No notebook-only models go to production.
Integration & rollout
Deployed behind a feature flag, exposed to a cohort, monitored for cost, latency, and quality. Rollback path documented before launch.
Drift & iteration
Drift monitors, eval re-runs, prompt and model upgrades, and a roadmap of small lifts that compound. The day-90 system should be cheaper and better than day-30.
The stack
Models, frameworks, and tools we ship with.
Model-agnostic by design. The list is short on purpose, and chosen for things we are willing to debug at 3 a.m.
FAQ
AI questions, answered honestly.
Do we need our own model?
Almost never to start. Most production AI today is a strong frontier model behind retrieval, tools, and a thin layer of your data. We will tell you when fine-tuning or training your own genuinely pays back—and when it is mostly bragging rights.
How do you handle hallucinations?
Grounding, tools, structured output, eval suites, and refusal behavior for things outside scope. The model is allowed to say "I do not know"—and good ones do.
How do you handle data privacy?
PII redaction, scoped retrieval, provider-terms review, and on-prem or VPC deployments where data residency requires it. We have shipped under HIPAA and SOC 2.
What about model drift?
Eval harnesses run on every release and on a cron. Drift in inputs, outputs, and downstream metrics is monitored. We schedule the model review before drift becomes a Slack thread.
Can you work with our existing data team?
That is most of what we do. We embed, transfer knowledge, write the runbooks, and leave behind something your team can run. We are happy to be temporary on purpose.
What does it cost?
Tailored to scope. There is no flat-rate menu. We size the work after a 15-minute discovery and a feasibility pass, never before.
Got a use case, a stalled pilot, or a model that needs a grown-up in the room?
Bring the data sample, the baseline you want to beat, and the dollar at stake. Fifteen minutes, real engineers, no slide deck.
Jacksonville (HQ) · Chicago · Nationwide