You've seen enough demos. What you need is an AI stack your team can own, extend and defend in a board review — with evaluation, guardrails and cost control from day one.
Ship AI capabilities into product and internal tools fast, without locking the company into a stack, a vendor or a set of prompts no one can maintain.
A clear framework for which layers to buy (models, vector DBs), which to build (retrieval, evals) and where to bring a partner.
One AI platform — auth, secrets, model routing, evals, observability — that every squad can build on.
Automated eval suites gating every prompt, model and pipeline change, like tests gate code.
Per-feature cost dashboards, latency budgets and fallbacks that keep AI features shippable at scale.
Route each request to the best-fit model (GPT, Claude, open-source) with fallbacks, retries and cost caps.
Hybrid retrieval, chunking that respects document structure, citations, and freshness controls the business can trust.
Typed tools, structured outputs, human-in-the-loop checkpoints — agents that stay in scope.
Prompt/response tracing, eval scores, cost per feature, drift alerts — one pane of glass across the whole AI surface.
A model or prompt change breaks a workflow no one is watching. Solved with evals in CI and canary rollouts.
One bad prompt loop bills five figures overnight. Solved with per-feature budgets and hard token caps.
You can't leave a model provider without rewriting half your app. Solved with a routing layer and portable prompts.
PII slipping into prompts, logs or fine-tuning data. Solved with redaction, DLP and audit trails.
Map the current AI surface: models, prompts, data, costs, incidents. Score risk and opportunity.
Stand up the shared platform: model router, RAG core, eval harness, observability, secrets and guardrails.
Migrate one product squad onto the platform, ship one net-new feature, and codify the playbook.
Production-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
Explore serviceMulti-agent architectures that plan, use tools and complete complex tasks.
Explore serviceAI-native automation that reads, decides and acts across your systems.
Explore serviceProduction ML for forecasting, churn, risk and pricing — trained on your data.
Explore serviceYes. Most CTO engagements are joint — our squads pair with your engineers, transfer patterns and hand over the platform cleanly.
Yes. We deploy into AWS, Azure, GCP, OCI, private cloud and on-prem. All patterns support strict data-residency and audit requirements.
We treat model choice as a hyperparameter. A routing layer picks the best-fit model per task with fallbacks, and evaluations decide when to switch.
Golden sets, LLM-as-judge, regression tests, cost and latency budgets, and canary rollouts — all wired into your CI/CD.
Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.