Hire an AI Developer -- or Hand Us the Whole Build
You searched for an AI developer. What you probably need is a shipped product: AI features that work, built by a senior team that uses AI tooling without shipping AI slop.
We build LLM features, RAG pipelines, and AI agents into production Next.js and Astro stacks. Skip the contractor vetting cycle: fixed quotes, senior engineers, code you can actually maintain after handoff.
Here's what hiring an AI developer should actually get you: shipped, reliable functionality that holds up when real users hit it. That might be a chat assistant that answers from your own data, document automation that doesn't fall apart on a messy PDF, or agent workflows with actual guardrails instead of just vibes. But honestly, the title "AI developer" covers two pretty different skill sets -- engineers who build AI features into products, and engineers who use AI tooling to build faster. The teams worth hiring do both. They're accountable for how the thing behaves in production, what tokens cost at scale, and what happens when it fails -- not just whether the demo looked good on a Tuesday afternoon Zoom call. That last part is where most hires go wrong. You get a working demo, everyone's excited, and then three weeks into integration you realize nobody thought about failure modes, cost ceilings, or what "done" actually means on real inputs.
What is holding your current website back?
Common gaps we find in nearly every audit.
What Your Website Could Look Like
Custom-designed for your industry. No templates. No stock photos.
How We Build This Right
Every safeguard, built in from Day 1.
No black-box handoffs
Every integration ships with documented architecture decisions, environment variable conventions, and inline comments explaining non-obvious prompt and retrieval logic so your team can own it after we leave.
Data handling boundaries stated upfront
We confirm which data touches third-party model APIs, what stays on your infrastructure, and where embeddings are stored before a line of code is written, so you can make informed compliance decisions.
Deterministic test coverage on AI paths
LLM outputs are non-deterministic, but the surrounding logic is not. We write unit and integration tests for retrieval pipelines, tool-call schemas, and fallback handling so CI catches regressions.
What We Build
Purpose-built features for your industry.
AI features built for production, not demo day
What we actually build: LLM chat grounded in your data, retrieval pipelines, document extraction, agent workflows. Each one ships with an evaluation suite, fallbacks, and monitoring. The definition of done here is behavior on real inputs. Not a recorded demo, not a clean notebook -- production behavior.
AI-assisted delivery on the whole build
We run Claude Code across the full stack every day, which is why our fixed quotes land at freelancer prices while still carrying agency accountability. The AI writes a lot of the code. Senior engineers own the architecture and review every single line before it ships. That's the arrangement, and it's why the economics work.
Cost engineering from the first commit
Model routing, response caching, and per-feature budget caps get wired in during the build itself -- not bolted on afterward. So when you launch, you already know what this costs per user and per feature. Alerts fire before spend drifts, not after your AWS bill arrives.
Rescue for stalled AI projects
We audit first: prompts, retrieval quality, agent loops, and spend. Then we deliver a fixed-price fix plan. Most rescues ship inside three weeks, because in practice the problem is almost always architecture -- not the model itself.
Plain-English delivery
Weekly updates written in product terms, a staging URL from week one, and documentation your next hire can actually onboard from without calling us. No black boxes. No dependency on us after handover. That's the standard.
Built on a Modern, Secure Stack
Our Development Process
From discovery to launch. Quality at every step.
Scope the outcome, not the tech
It's a 2 to 3 day sprint. We define what the AI feature needs to do on real inputs, what failure actually looks like, and what this will cost per month at scale. You approve the spec and the fixed price together before anything gets built.
Build against an evaluation suite
We write the eval cases before the feature. Representative inputs, edge cases, red lines -- all of it defined upfront. Every iteration gets measured against them. That's the difference between engineering and prompt-tinkering, and it's not a small difference.
Ship behind a kill switch
Staged rollout with monitoring on quality, latency, and spend. If any metric crosses the agreed line, the feature degrades gracefully instead of taking down the product with it. That's the deal.
Handover with the levers labeled
You get the repo, prompts, evals, dashboards, and a working session with your team on how to tune each part. Plus 30 days of post-launch cover, included in the fixed price. Nothing locked away, nothing that requires us to operate it.
Ready to discuss your hire an ai developer -- or hand us the whole build project?
Get a free quoteFrequently Asked Questions
Explore related industries
200+ employee company? Complex multi-tenant, auction, or multi-location requirement? We have a dedicated enterprise capability track.
Tell us about your project
We reply within one business day with a scoped, fixed-price plan.
Let's build
something together.
Whether it's a migration, a new build, or an SEO challenge — the Social Animal team would love to hear from you.