Production AI Infrastructure From People Who Ship It Themselves
We're an AI startup too. Eval frameworks, model orchestration, RAG, cost controls, agent observability — the boring scaffolding that separates demo AI from production AI.
Our typical AI startup client
Stage
5–50 people, pre-seed to Series B
Stack
OpenAI/Anthropic + Postgres + Stripe + Vercel/AWS + HubSpot + Notion
Pattern
AI-first product, often customer-facing, shipping weekly
Common situations
What's Broken in AI Startup Engineering
Eight patterns we see in almost every AI-first team. If you've hit any of these, the next thing to fix is probably scaffolding, not more model capability.
Your AI inference bill doubled this month and you're not sure why.
Tokens add up fast — especially when a prompt regression sends every request to GPT-4. We build per-feature dashboards + budget caps + automatic model-tier fallback so cost stays predictable without sacrificing the quality of the high-stakes paths.
Your eval framework is "vibes on three test prompts."
You change a prompt, it feels better, you ship. Two days later a customer reports a regression on a use case you forgot to test. A real eval suite — golden dataset, automated regression run on every prompt change, CI integration — turns prompt engineering from intuition into engineering.
Hallucinations slip through to customers.
Your RAG is bolted together with chunk size 1000 and the first embedding model you tried. Customers report "made-up" answers. The fix is architectural: better retrieval, structured output schemas that force the model to admit uncertainty, confidence-routed human review on low-confidence outputs.
Agentic workflows fail silently in production.
Your agent orchestrator works in dev. In prod it loops, retries forever, or returns half-completed work without flagging failure. Observable agents need explicit state machines, step-level timeouts, structured failure reasons, and a human-readable trace per run.
You're a data team that suddenly has to ship a product.
Your DBT pipelines are perfect. Your customer-facing app is a notebook with a Streamlit frontend. We build the production wrapper — auth, multi-tenancy, billing, error handling, deploy infrastructure — so your ML work has a real surface to live in.
Your customer-facing AI feature got built by your CTO over a weekend.
It demoed great. Now it's in front of paying users and there's no eval, no cost controls, no audit log, no human-in-the-loop fallback. We harden weekend-prototype AI into production AI without rebuilding it from scratch.
You picked a model 6 months ago and never re-benchmarked.
Claude Sonnet exists now. So does GPT-4.1, Gemini 2.5, Llama 4. Your locked-in model is twice the cost and worse quality than what shipped last quarter. A provider-abstracted architecture + ongoing eval makes model swaps a config change, not a rewrite.
Your investors want monthly metrics; your metrics live in 4 SaaS dashboards.
Stripe revenue here, Mixpanel events there, OpenAI usage in a separate console, HubSpot pipeline in a fourth. A unified investor-grade dashboard pulled from all four into one Postgres + one React app — usually 2-3 weeks of work for a real return.
Deliverables
What We Ship for AI Startups
Six engagement patterns we've shipped for AI-first teams. Most start with eval framework + cost controls as the foundation.
Model orchestration + provider abstraction
Thin adapter over OpenAI / Anthropic / Gemini / open-source. Switching providers is a config change. Multi-provider failover for uptime. Cost-tier auto-routing based on request shape.
Eval framework + golden dataset
Golden test set committed to repo. Automated eval run on every prompt or model change. CI fails on regression. Surfaces which examples got better, which got worse — no more vibes-based prompt engineering.
RAG infrastructure
Vector store + hybrid search + reranking + chunking strategy tuned to your data. Citations on every response. Confidence scoring routes uncertain answers to a human queue.
Agentic workflow orchestration
Step-level state machines, timeouts, structured failure modes, human-readable traces. Agent runs are debuggable instead of black-box hopeful.
Cost controls + observability
Per-user, per-feature token budgets. Real-time cost dashboards. Automatic fallback to cheaper models when the expensive tier isn't justified. Alerts on anomalous spend.
Customer-facing AI hardening
Auth, multi-tenancy, rate limiting, audit logs, streaming UI, error handling — the scaffolding that turns weekend-prototype AI into production AI.
Related services
Services That Fit This Vertical
Four services apply most directly to AI startups. AI Integration is the foundation; the others compound on top.
AI Integration & Automation
The primary engagement for AI startups: eval frameworks, RAG, cost controls, agent orchestration. Production-grade scaffolding around the LLM.
Custom Web Applications
When the AI feature needs a real product surface — auth, multi-tenancy, billing, customer dashboards, admin console.
System Integration
AI features need data. We connect Stripe + HubSpot + your warehouse + the LLM into one production pipeline.
Data Analytics & Dashboards
Investor metrics, AI cost attribution, eval quality trends, customer usage cohorts — the dashboards every startup wishes it had.
Tech stack
The AI Startup Stack
Tools we've worked in across many AI startup engagements. If yours isn't listed, we still probably know it.
LLM providers
- OpenAI
- Anthropic Claude
- Gemini
- Llama / Mistral (self-hosted)
Orchestration
- LangChain
- LlamaIndex
- Custom (often simpler)
- Inngest / Trigger.dev
Vector / RAG
- Pinecone
- Weaviate
- pgvector
- Chroma
App stack
- Next.js
- Vercel / AWS / Fly
- Postgres / Neon
- Clerk / WorkOS
Billing & growth
- Stripe
- HubSpot
- Mixpanel / PostHog
- Loops
Ops
- Notion
- Linear
- Slack
- Sentry
AI Startup Case Study
Portfolio Command Center for Consulting
How Purcell Analytics built the Autonomous AJ Command Center — a Django + FastAPI + React platform that runs portfolio-wide content pipelines, GSC monitoring, financial dashboards, and continuous project-readiness audits across every site in the portfolio.
Industry
Internal operations platform
Company size
small consulting practice with 14+ properties
Stack
Django 5.2, FastAPI, React, TypeScript
Playbooks
34 Documented Playbooks for AI Startups
Automation patterns we've documented for AI-first teams. Use them to scope engagements or brief your team.
GDPR Compliance for AI Startups | Purcell Analytics
Practical playbook for automating GDPR & Data Privacy Compliance in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
CSAT Surveys for AI Startups | Purcell Analytics
Practical playbook for automating NPS & CSAT Surveys in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
Account Health for AI Startups | Purcell Analytics
Practical playbook for automating Customer Health Scoring in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
HR Onboarding for AI Startups | Purcell Analytics
Practical playbook for automating Employee Onboarding in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
Knowledge Base for AI Startups | Purcell Analytics
Practical playbook for automating Knowledge Base Management in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
KPI Alerts for AI Startups | Purcell Analytics
Practical playbook for automating KPI Alerting & Anomaly Detection in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
Renewals for AI Startups | Purcell Analytics
Practical playbook for automating Contract Renewals in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
Vendor Mgmt for AI Startups | Purcell Analytics
Practical playbook for automating Vendor Management in AI & Data Startups. Pain points, typical tools, and measurable outcomes.
FAQ
Frequently Asked Questions
We're 8 people and pre-seed. Are we too early?
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Probably yes for a $25K custom build. But focused engagements ($8-15K) for shipping a customer-facing AI feature with proper eval + cost controls absolutely fit pre-seed stage. We'll be honest about right-sizing.
We already use LangChain / LlamaIndex — do you replace it?
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Usually no. We work with what you have unless the abstraction is actively in the way. For many startups, a thinner custom orchestration ends up simpler — but the existing framework choice is rarely the blocker.
Can you help us migrate from OpenAI to Claude (or vice versa)?
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Yes — common engagement. The first step is usually building the eval harness so the migration has a quality bar, not just a cost target. Then the migration itself is mostly the easy part.
We need SOC2. Can you ship AI features that pass audit?
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Yes. Several of our AI startup engagements have shipped under SOC2 or HIPAA constraints. Audit logging, PII handling, model selection (often local/open-source for sensitive paths), and access controls are designed in from day one.
Do you help with prompt engineering specifically?
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We'll build the eval harness + golden dataset that lets your team do prompt engineering properly. We won't write your prompts in a vacuum — your team knows the use case best. We make the engineering loop work.
What about agentic frameworks — CrewAI, AutoGen, OpenAI Agents SDK?
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We've shipped on all three plus custom orchestration. The framework choice matters less than the observability + failure handling + cost controls around it. We'll pick the simplest fit for your use case.
We want to fine-tune. Do you do training?
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Sparingly. For most AI startups, prompt engineering + RAG + structured output gets 90% of the value at 5% of the cost. We'll do fine-tuning when it's genuinely justified (large stable use case, latency requirements) but push back when it's premature optimization.
What's your largest AI startup engagement?
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Multi-month engagements ranging $50-100K covering the full production-AI stack: orchestration, eval, RAG, cost controls, customer-facing app, observability. Most engagements are smaller — a $15-25K focused project on one production-readiness problem.
Calculators
Run the Numbers on Your AI Project
AI Automation ROI
Most relevant calc. Labor savings minus AI infrastructure cost = honest net benefit.
Build vs SaaS Calculator
For startups deciding between building auth/billing/observability vs renting (Clerk, Stripe Billing, Sentry).
Process Automation ROI
For internal ops automations (customer onboarding, support triage, content workflows).
Which AI Production Problem Is Slowing You Down?
Schedule a 30-minute AI strategy call. We'll review your setup and send a written recommendation on the highest-ROI next move within a week.