AI Workflows That Actually Make It to Production
We integrate OpenAI, Anthropic, and open-source LLMs into your real workflows — with eval frameworks, cost controls, audit logging, and human-in-the-loop where it matters.
We ship the same AI stack we run for ourselves
Common situations
Where AI Actually Pays Off
Six patterns where AI integration delivers real ROI in months, not years. If you see your situation here, an AI engagement is probably overdue.
You tried ChatGPT for the workflow and it almost worked.
The demo was impressive. Then you realized the output isn't deterministic, the prompt breaks when inputs change, there's no audit trail, and nobody knows what to do when it hallucinates. Production AI needs scaffolding around the LLM, not just a clever prompt.
Your LLM costs are unpredictable and rising.
A workflow that cost $40 last month costs $400 this month and you don't know why. We build cost-controlled pipelines with token budgets, response caching, model-tier selection by request type, and per-feature dashboards so you see exactly where the money goes.
You need extraction from messy unstructured documents.
PDFs, scanned contracts, customer emails, support tickets, vendor invoices — and your team is reading every one. Modern LLMs handle this well when wired into a pipeline with confidence scoring and human-in-the-loop fallback for low-confidence extractions.
Reports take 4 hours every Monday because someone has to write the narrative.
The numbers come from your dashboard; the *interpretation* still lives in a human's head. A well-scoped LLM summarization layer can draft the narrative in seconds — a person edits in minutes instead of writing from scratch in hours.
Customers ask the same 20 questions and your team answers each one.
A retrieval-augmented assistant grounded in your actual product docs + past tickets can handle the bulk of tier-1 with citations back to source. Not a generic chatbot — a system that says "I don't know" instead of making things up.
You want to try AI agents but don't know what's real vs hype.
We've built and shipped agentic systems in production (PA itself runs on agent-orchestrated content + ops pipelines). We can tell you honestly which agent patterns are production-ready today and which are still demos.
Deliverables
What's Included
Every AI engagement ships with the same production-grade backbone. Speculative AI experiments live elsewhere — this is for workloads going live.
Use-case discovery + ROI scoping
We map the manual work, estimate hours saved per week, and tell you which AI applications will actually pay back vs. which are nerd-candy. Honest assessment before we build.
Model selection (OpenAI / Anthropic / open-source)
GPT-4 isn't always the right answer. We benchmark Claude, GPT-4, Gemini, and open-source options against your actual data to pick the right tradeoff between quality, cost, and latency.
Prompt engineering with eval framework
Prompts shipped to production have a regression test suite. When you change a prompt, you see immediately which examples got better and which got worse — not just "vibes" on three test runs.
Retrieval-augmented generation (RAG)
Vector store setup, chunking strategy, embedding model selection, hybrid search where it helps. Grounded responses with citations back to source documents.
Cost controls + token budgets
Per-user, per-feature, and per-request budget caps. Auto-fallback to cheaper models when the expensive path isn't justified. You'll never get a surprise $5K OpenAI bill.
Confidence scoring + human-in-the-loop
Every AI output gets a confidence score. High-confidence flows through automatically; low-confidence routes to a human review queue. Configurable thresholds per workflow.
Streaming UI where it matters
Token-by-token streaming for chat, batch processing for back-office. We pick the right pattern per workflow — users don't wait 30s for a response that could stream.
Audit log of every AI decision
Input, output, model, version, prompt, token count, latency, cost — logged per request. Required for compliance, useful for debugging, essential for cost attribution.
Eval harness for ongoing quality
Golden-dataset evals you can run when models drift or you tweak prompts. Catches regressions before users do.
Production deploy + monitoring
CI/CD with prompt + eval tests gating deploy. Per-model dashboards (latency, error rate, cost, token volume). Alerts when something drifts.
Process
How We Build It
Five stages. The prototype in week 1 keeps scope honest — you see real outputs before we commit to the full build.
Scope
We sit with the team doing the manual work, map the decision tree, and pick the highest-ROI use case. The first AI project should pay for itself in months, not years.
Prototype
Working prototype on real data in week 1–2. You see actual outputs (good and bad) before we commit to the full build — so the scope stays honest.
Build
Production pipeline: model selection, prompt eval suite, RAG infrastructure, cost controls, audit logging, human-in-the-loop where confidence dictates.
Deploy
Behind a feature flag. We run dual-track (AI suggests, human decides) for the first 1–2 weeks to calibrate confidence thresholds against real outcomes.
Operate
Monitoring dashboards live. Runbook for cost spikes, prompt regressions, model deprecation. 30-day operational support to catch what only shows up under real load.
Comparison
How We Compare
Three honest ways to get AI into your business. Pick the one that matches your stakes.
ChatGPT / Claude in a browser
- Time to first outputMinutes
- DeterminismNone
- Cost predictabilityManual
- Audit trailNone
- Eval frameworkNone
- Scales past one userNo
No-code AI platforms
- Time to first outputDays
- DeterminismSome
- Cost predictabilityPer-seat
- Audit trailBasic
- Eval frameworkLimited
- Scales past one userWith $$$
Purcell Analytics
- Time to first outputPrototype in week 1
- DeterminismEngineered in
- Cost predictabilityBudgeted + capped
- Audit trailEvery request logged
- Eval frameworkRequired, in CI
- Scales past one userDesigned for it
Case study spotlight
Programmatic SEO Content Pipeline
How Purcell Analytics built a programmatic SEO pipeline that publishes thousands of structured-data-first pages across the portfolio at $0 LLM cost — entity model, quality gates, internal linking, hub generation, and GSC-driven LLM upgrade path.
Industry
Internal content engineering
Company size
portfolio of 14+ sites
Stack
Django 5.2, Celery, PostgreSQL, Anthropic SDK
Tech stack
The AI Stack We Build On
Provider-agnostic by design. Switching models is a config change, not a rewrite.
LLM providers
- OpenAI (GPT-4, GPT-4o, GPT-4 Turbo)
- Anthropic (Claude Opus, Sonnet, Haiku)
- Google (Gemini Pro)
- Open-source (Llama, Mistral via vLLM/Ollama)
AI orchestration & RAG
- LangChain, LlamaIndex (where it earns its weight)
- Custom orchestration (often simpler + faster)
- Pinecone, Weaviate, pgvector
- Hybrid search (semantic + BM25)
Backend + ops
- Django + Celery for async pipelines
- Doppler for secrets
- Buildkite / GitHub Actions
- Custom eval dashboards
- Per-model cost + latency monitoring
FAQ
Frequently Asked Questions
Which LLM provider do you recommend?
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Depends on the workload. Claude (Anthropic) tends to win on long-context, careful-reasoning, and writing tasks. GPT-4 wins on broad availability, tool-use, and the largest ecosystem. We benchmark both against your actual data before choosing — and often run multiple providers in production for redundancy and cost optimization.
How do you keep AI costs from spiraling?
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Per-request token budgets, per-feature monthly caps with auto-fallback to cheaper models, response caching for repeated queries, and a real-time dashboard so you see spend trends before they become bills. We've prevented multiple six-figure surprise bills for clients.
What about hallucinations?
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Hallucinations are a tool design problem, not an LLM problem. We engineer around them: retrieval-augmented generation grounds responses in source docs, structured output schemas force the model to either produce valid output or fail, and confidence scoring routes uncertain outputs to humans.
Can you run AI on our own data without sending it to OpenAI?
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Yes. Open-source models (Llama, Mistral) run on your hardware or in your VPC. We've shipped local deployments for clients in healthcare and finance where data residency is non-negotiable. The quality gap to GPT-4 has narrowed significantly in 2026.
Are AI agents production-ready?
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Some patterns are, some aren't. Single-step agents with tool use (call this API, parse the response, return) are solid. Multi-step planning agents are still flaky in production for high-stakes work. We'll tell you honestly which side of the line your use case falls on.
How long until I see ROI?
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For the right use case, weeks. The 4-hour-Monday-report use case typically pays for itself inside the first quarter. Speculative AI work that 'might be useful someday' should not be your first project — we'll steer you to a workload with a measurable hour-savings number.
What if OpenAI changes their pricing or deprecates a model?
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We design with provider abstraction. Switching from GPT-4 to Claude Sonnet is a config change + an eval re-run, not a rewrite. Multiple clients run with provider failover so model deprecation never takes them down.
Do you do agentic AI / Claude Skills / sub-agent orchestration?
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Yes. PA itself runs on agent-orchestrated content + ops pipelines (we use AACC for our own programmatic SEO work). We've built sub-agent orchestration, plan-and-execute systems, and tool-use loops in production.
Related Services
Process Automation
AI is one tool; deterministic workflow automation is another. We pick the right one per step.
System Integration
AI needs data. We connect the systems first, then layer intelligent automation on top.
Custom Web Applications
Full-stack apps with AI woven in — chat UIs, document review tools, agent dashboards.
More Case Studies
Programmatic SEO Content Pipeline
AI-driven content generation pipeline producing thousands of pages at consistent quality.
AACC Portfolio Command Center
Multi-agent orchestration platform managing 14+ web properties via Claude + Django.
QuantAIze
AI-powered geocoding + analytics platform with custom LLM pipeline for unstructured data.
Ready to Put AI Into Production?
Schedule a 30-minute AI strategy call. We'll discuss your highest-ROI use case + send a written assessment within a week — whether you hire us or not.