Costs that scale badly
Token spend growing faster than usage because entire documents are sent on every request.
[ AI & Automation / LLM & AI Integration ]
AI features in your product, not a science project.
Calling a model API takes an afternoon. Making it reliable, affordable and safe under real usage is the actual work — retries, streaming, cost ceilings, and knowing what the feature does when the provider has an outage.
Cost modeled upfront · Fallbacks included · Fixed scope
Modeledcost before you commit
Fallbacksfor provider outages
Evaluatednot vibes-tested
Portableswap models later
[ What goes wrong ]
How AI features fail after launch.
The feature usually ships fine. What follows is the part nobody planned for.
Slow, blocking responses
Users staring at a spinner for fifteen seconds because nothing is streamed.
No handling for failures
A provider timeout takes down a page, because the integration assumed the call always succeeds.
Unpredictable output
Free-form text where structured data was needed, breaking whatever consumes it downstream.
Prompt injection
User input treated as trusted instruction, so people can talk the feature into ignoring its rules.
No quality measurement
Prompt changes shipped on instinct, with no way to tell whether they improved anything.
[ How it works ]
Prototype, harden, ship.
01 — Week 1
Define
What the feature does, what good output looks like, and a cost model at your expected volume. Some ideas do not survive this step, which saves you a build.
02 — Week 2-3
Build
Structured outputs, streaming, retries, timeouts, caching and cost ceilings — the production layer around the model call.
03 — Launch
Evaluate
Scored against a test set, deployed behind a flag, with logging and spend alerts so surprises surface in hours.
[ What actually changes ]
The production layer.
The model call is a few lines. Everything below is what separates a feature you can leave running from one that needs babysitting.
Reliability
- Structured outputs — schema-validated responses, so downstream code is not parsing prose.
- Streaming responses — output appears as it generates instead of after a long silence.
- Retries and timeouts — with backoff, so a slow provider degrades the feature rather than the page.
- Fallback behavior — a defined answer to what happens when the provider is down.
- Provider abstraction — so switching models later is a configuration change, not a rewrite.
Cost & safety
- Prompt caching — reusing the stable parts of prompts to cut spend substantially on repeat calls.
- Context trimming — sending what the task needs, not every document you happen to have.
- Spend limits and alerts — per user, per feature, with a hard ceiling and notification.
- Injection defenses — user input separated from instructions, with output validated before use.
- Evaluation set — so a prompt change can be scored rather than argued about.
[ Example engagement ]
Same feature, a fraction of the spend.
A summarization feature sending full documents on every request with no caching. Restructured with context trimming, prompt caching and a smaller model for the easy cases.
Illustrative example of a typical engagement. Figures vary with the state of your systems and are not a guarantee of a specific outcome.
78%lower monthly spend
3.4xfaster median response
0quality regression
1 wkof work
[ Free · honest answer ]
Should this be an AI feature at all?
Describe what you want it to do. If a simpler approach would work better or cheaper, I will tell you that instead of building it.
[ Pricing ]
Pricing that fits your budget.
Tell me the number you have to work with. I'll tell you honestly what's achievable within it — and if it isn't enough, I'll say so before we start rather than halfway through.
Fixed project price
Scope agreed in writing, price agreed in writing, before any work starts. No hourly creep and no invoice you haven't already approved.
Monthly retainer
For ongoing work — maintenance, monitoring, updates and small changes. Month to month, cancel whenever, no minimum term.
Hourly for small jobs
For a single bug or a short task where writing a full scope would cost more than simply doing the work.
Budget too tight for the whole thing? I'll often suggest doing the highest-value part first and the rest later, rather than doing all of it badly.
[ Questions ]
Common questions.
Which provider should we use?
It depends on your accuracy needs, latency, budget and data residency requirements. I build against an abstraction so you can switch later, because pricing and capability shift constantly in this space.
How much will it cost to run?
Modeled before the build from your expected volume and prompt sizes, then controlled with caching, trimming and hard ceilings. You get the estimate before committing, not after the first invoice.
Is our data safe?
Enterprise API tiers generally do not train on your inputs, and regional processing options exist where data residency matters. I configure that explicitly and document exactly what is sent.
Can we run a model ourselves?
Yes, and occasionally it is right — strict data rules or very high steady volume. For most teams the operational cost of self-hosting outweighs the saving, and I will run the numbers honestly.
How do we stop it saying something embarrassing?
Constrained outputs, validation before anything is displayed or acted on, and a test set covering the awkward cases. No approach is perfect, so features that carry real risk get a human in the loop.
[ Related services ]
Related AI work.
AI RAG ChatbotAssistants that answer from your documents, with citations.AI Agent DevelopmentAgents that use tools and do work, with guardrails that hold.n8n & Workflow AutomationConnect your tools and delete the copy-paste work.Python & FastAPITyped, async Python APIs and data services.Node.js DevelopmentAPIs, services and background workers that stay up.Next.js DevelopmentApp Router, rendering strategy and Core Web Vitals done right.
[ Let's talk ]
Tell me what you're building.
Describe it in a few lines and you'll get a straight answer on scope, cost and timeline — same working day, from the person who'd actually build it.