Applied AI
AI in real workflows.
Not in slide decks.
Applied AI is the integration of LLMs, RAG, and agents into the workflows your business actually runs. We identify the highest-leverage opportunities, ship the integration to a production bar, and stand up the monitoring + cost discipline that makes AI a durable line item rather than a recurring experiment.
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Applied AI is the engineering work to integrate AI into operations: LLM-powered workflows, retrieval-augmented generation (RAG), agentic processes, and the observability + cost discipline that keeps it production-grade. The deliverable is shipped capability, not a strategy doc.
Specific outcome
AI shipping in production, with cost + quality discipline.
High-leverage workflows automated. Monitoring + cost guardrails wired. Defensible, auditable, durable.
Operational credibility
How we run.
Production bar, not demo bar
Most AI work decays at the prototype-to-production boundary. We ship to production discipline from day 1.
Workflows · RAG · Agents · Ops
Four shapes, all production-grade. We don't sell AI as a wrapper around someone else's model.
Code, runbooks, monitoring
You own the integration. Cost guardrails, prompt registry, evaluation framework documented.
The system
Six modules. One production AI integration.
Opportunity mapping
Identify the 3–5 workflows where AI compounds. ROI estimates. Sequencing by impact + dependency.
LLM-powered workflows
Customer support triage, content drafting, summarization, classification. Prompts versioned. Output verified. Cost monitored.
RAG + knowledge retrieval
Vector store + retrieval over your knowledge base. Citation discipline. Evaluation framework. No hallucinated answers in production.
Agentic processes
Multi-step agents for research, data prep, internal automation. Sandbox boundaries. Observability. Human-in-the-loop where it matters.
AI ops + cost discipline
Token monitoring + alerting. Cost-per-conversation budgets. Model routing (cheap-first, escalate when needed). Eval framework.
Quality measurement
Eval suite: factuality, helpfulness, refusal correctness. Drift detection. Continuous improvement loop.
What we do
Every engagement, in writing.
- 01Map the highest-leverage AI opportunities in your workflows; prioritize by ROI + dependency.
- 02Architect LLM-powered workflows with prompt registry, model routing, and cost guardrails.
- 03Build RAG over your knowledge base with citation discipline and an evaluation framework.
- 04Ship agentic processes for multi-step automation where the leverage warrants the complexity.
- 05Stand up AI ops: token monitoring, cost alerting, drift detection, eval suite.
- 06Train your team to operate the integration; document the runbook.
WHEN IT FITS
- +You see specific operational workflows where AI would compound — triage, drafting, summarization, retrieval.
- +You need an AI integration that survives production traffic and audit scrutiny.
- +You've experimented with AI features but they didn't make it past prototype.
WHEN IT DOES NOT
- −You're looking for an "AI strategy deck." We ship integrations; pure-strategy work isn't our shape.
- −You want to build a foundational model. We work at the application layer; foundation-model R&D is different work.
Architecture
Applied AI as one of six interlocking operating layers.
Implementation
How a typical run sequences out.
Two-week opportunity mapping + architecture: workflows identified, ROI modeled, integration scoped.
4–16 weeks depending on scope. First production-grade AI workflow live by week 4. Vertical slices ship continuously.
Code, runbooks, eval suite, cost monitoring. Optional Embedded Retainer for ongoing AI engineering.
FAQ
Questions we get asked.
01Which models do you use?+
We pick the right model for the job — OpenAI, Anthropic, open-source via Together / Replicate / self-hosted. Model routing means cheap models handle most traffic; expensive models escalate when needed.
02How do you handle hallucinations?+
Citation discipline + RAG + eval framework. We design AI workflows where hallucinations are caught (eval suite, human-in-the-loop) before they reach the customer. Production-grade ≠ "trust the LLM."
03What about cost?+
Cost-per-conversation budgets, model routing, and prompt optimization keep cost predictable. We have shipped AI integrations at <$0.05/conversation that previously cost $5+ per human interaction.
04How do you measure quality?+
Eval suite per workflow: factuality, helpfulness, refusal correctness. Drift detection on production traffic. We don't ship AI features without measurement infrastructure.
05Will this work with our existing stack?+
Almost always yes. We integrate AI as a layer, not a replacement. Have shipped on top of HubSpot, Salesforce, GHL, Supabase, Postgres, and custom backends.
06What about prompt injection / security?+
Architected from day 1. Sandboxed agents, validated outputs, no PII leakage, prompt-injection defense, audit logs. Security review part of every engagement.
07How is this different from "we use ChatGPT"?+
ChatGPT is a tool we may use. The engagement is the engineering: integration into your workflows, RAG over your knowledge, eval suite, cost guardrails, monitoring. Tools without architecture stay at the experiment stage.
08How does this fit with our Marketing / RevOps systems?+
Often the highest-leverage AI workflows are inside marketing + RevOps. We pair Applied AI with the broader systems work where the unit economics support it.
Related
Adjacent services and playbooks.
Get started
Ship AI in production. Not in slides.
A strategy call gets you a tailored scoping doc and pricing within 48 hours.