Hire AI developers who ship features that hold up in production
AI features demo beautifully and break quietly - hallucinations, cost blowups, prompt-injection, no evals. Codivox gives you a senior AI team that ships LLM features with the guardrails and observability that keep them reliable.
The short answer
AI developers are the highest-rate niche in 2026 - $80-$250/hr freelance, or $200k-$350k+ total comp for a US senior FTE, and they're hard to vet if you're not technical. A managed AI team like Codivox ships production AI features - RAG, agents, LLM integrations - with evals, guardrails, and cost controls, at a fixed scope, so you get reliable features instead of a fragile demo.
Don't hire a AI seat. Hire the shipped outcome.
A single hire is one dependency for skill coverage, availability, and continuity - and you still own the roadmap, the reviews, and the delivery risk. A senior team with AI acceleration ships more than a lone hire, owns the result, and doesn't stall when one person is out.
What our AI engagements deliver
LLM features that ship
RAG, chat, extraction, classification, and agents wired into your product with validation and safe failure modes.
Guardrails + evals
Human-in-the-loop where risk is high, eval suites so quality is measured, and prompt-injection defenses - not vibes.
Cost + latency control
Model routing, caching, and token budgets so an AI feature doesn't quietly blow up your bill or your p95.
Observability
Tracing, logging, and dashboards for every AI call, so you can see what the model did and why.
Why AI projects stall - and how we prevent it
The failure modes we see most when AI work is handed to a single hire or an unmanaged contractor - and the way a senior team heads each one off.
The demo dazzles, then hallucinates in production
How we prevent it: We ground responses in retrieval, validate outputs against types and schemas, and gate high-risk actions behind human review - so what ships is measured for accuracy, not judged by a happy-path demo.
Token costs and latency quietly spiral out of control
How we prevent it: Model routing to cheaper models where they suffice, caching, and token budgets with monitoring keep spend and p95 latency predictable instead of surprising you on the invoice.
Prompt injection and no evals leave quality unmeasured
How we prevent it: Input sanitization, prompt-injection defenses, and eval suites that run on every change mean quality is tracked over time - not a vibe that silently regresses with the next model update.
What hiring AI developers actually costs
| Option | Cost | What it really means |
|---|---|---|
| US senior AI/ML FTE | $200k-$350k+ total comp | Scarce, slow to hire, hard to vet |
| AI freelancer | $80-$250/hr | Quality and eval discipline vary widely |
| Dedicated offshore AI dev | ~$8k-$16k/mo | Coordination + ramp on top |
| Codivox AI team | Fixed-scope sprints | Senior team owns reliable delivery |
Directional 2026 ranges; vary by region, seniority, and scope. Rephrased for compliance.
What we build with
Best for: Teams adding AI features to a real product that need them reliable, observable, and cost-controlled - not a fragile proof of concept.
What teams hire our AI developers to build
- RAG and semantic search over your data
- Support copilots and in-app assistants
- Document extraction and summarization
- Classification, routing, and enrichment
- Agents and multi-step AI workflows
- Adding LLM features to an existing product
Hiring AI developers, answered
How much does it cost to hire an AI developer in 2026?
AI is the highest-rate developer niche: freelancers run $80-$250/hr and a US senior AI/ML engineer is $200k-$350k+ in total comp - and they're scarce and hard to vet without deep technical knowledge. A managed AI team like Codivox prices by scope and owns quality, so you get reliable AI features without competing for rare hires.
What AI features can you build?
RAG and semantic search, chat and copilots, document extraction and classification, agents and workflows, and LLM integrations into existing products - all with evals, guardrails, and cost controls so they hold up in production.
How do you keep AI features from hallucinating in production?
Typed inputs and validated outputs, retrieval grounding, human-in-the-loop gates on high-risk actions, eval suites that measure quality over time, and prompt-injection defenses. AI drafts; guardrails and senior review decide what ships.
Can you control AI costs?
Yes - model routing (cheaper models where they suffice), caching, token budgets, and monitoring so an AI feature doesn't quietly blow up your bill or latency.
Can you add AI to our existing product?
Yes. We regularly add AI features to live products, wired in with observability and guardrails, without destabilizing what already works.
Do we own the code and prompts?
Yes - all code, prompts, eval suites, and infrastructure, documented and free of lock-in.
Which models and AI tools do you build with?
OpenAI, Anthropic, and open models depending on the task, with RAG on pgvector, orchestration in LangChain or custom code, and tracing plus evals throughout. We stay model-agnostic so we can route to whatever gives you the best quality-per-dollar, and swap as the landscape shifts.
How do you keep our data private and secure?
We use providers and configurations that don't train on your data, keep sensitive context server-side, scope retrieval to what a user is allowed to see, and log AI calls for audit. Data handling is scoped explicitly at the start of the engagement.
Hiring for a different role or stack?
Ready to ship your next product?
Tell us what you're building. Senior engineers will scope, plan, and start delivering your product with production-ready architecture - fast.