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01 · AI · Marquee practice

01 · AI consulting & build

AI that earns its place in your business.

From the owner asking where AI genuinely helps, to the platform team putting agents into production — strategy, enablement, build, and operation, sized to the value at stake. Some of our best advice is what not to build.

a
AI readiness assessment
A short, fixed-scope look at your business: which workflows are worth automating, which to leave alone, and what it would cost. Vendor-neutral, in plain language. No cloud migration required — sometimes the answer is an off-the-shelf tool, and we'll say so.
b
Use-case strategy
Three to five specific use cases ranked by value and risk — not a transformation deck. A roadmap your team can execute with or without us.
c
Team enablement & AI policy
Training your people on the tools they already touch, and a usage policy that protects your data and your clients. The gap is rarely the technology — it's confidence.
d
Data readiness
What your data can support today, what it can't yet, and the shortest path between the two. Collection, quality, and governance fixes scoped to the use cases that matter.
a
Agentic systems
Multi-tool agents with grounded retrieval, tool-use loops, and explicit failure modes. We design the recovery path before the happy path. Audit trails on every action.
b
Retrieval-augmented generation
RAG that holds up under adversarial queries. Hybrid retrieval, freshness controls, reranking, and evaluation harnesses that compare against your real workload — not a benchmark.
c
Model deployment
Vertex AI, Gemini API, and self-hosted endpoints — chosen for fit, not loyalty. Cost, latency, and routing engineered against actual traffic shapes.
d
Evaluation and observability
We start at the eval loop — where most teams stop. Continuous offline + online evals tied to deployment gates. Latency, accuracy, refusal, and grounding observed on the same dashboard as the rest of the platform.
i
Readiness
Two to three weeks, fixed scope. You leave with the assessment and the ranked use cases; no obligation to build anything.
ii
Enable
Team training, AI usage policy, and data groundwork — for organizations that want their people fluent before their stack changes.
iii
Pilot and build
A governed pilot with explicit go/no-go gates, then full agent or RAG delivery. Eval design and baseline measurement set the bar before anything ships.
iv
Operate
EverForge takes the on-call rotation. SLOs on the eval metrics, not just uptime.

Sizing up AI for the first time, or shipping agents to production — either way, we'll talk value before architecture.