Most "AI strategies" we see are slide decks. We ship working systems. RAG over your corpus, automation that actually fires, evaluations that catch regressions before customers do, and a clear handoff between the agent and the human when the agent should not have the wheel.
Customer support deflection, ticket triage, internal knowledge agents, document workflows, contract review pipelines.
Content engines, personalization, lifecycle automation, lead scoring, SEO and SERP testing at scale.
Creative generation pipelines, bid logic, audience inference, attribution model integration, dashboarding.
Phone agents, IVR replacement, multilingual call routing, real-time transcription, and the call analytics that go with them.
RAG over your corpus, multi-agent systems, evaluations and guardrails, internal copilots, developer tooling.
Two-week engagement. Map the data, score the use cases by ROI and feasibility, write the build sequence.
Before a single agent ships, we set up a graded eval set against your real data. If the model score drops on a future change, we know before you do. Most teams skip this because it is unglamorous. It is also the difference between an AI that works in a demo and one that works in production for two years.
Every agent has a clear escalation path to a human, with structured context handoff so the human is not starting from scratch. We build the queue, the SLA timer, and the feedback loop that turns the human's correction back into a training example.
We pick the model that wins for the task, not the model that won the last RFP. Claude for long-context reasoning, GPT-4 for tool calling, open-weight Llama or Qwen on your own hardware when latency or data residency matter, embedding models that fit your retrieval pattern, not vice versa.
A real engineer reads what you sent. No discovery call to gate a discovery call.