AI
AI knowledge platform rebuilt on Next.js + RAG
A product team replaced a brittle Python knowledge surface with a grounded Next.js and RAG stack to improve onboarding and support resolution.
Stronger onboarding and lower support load
/ Hire AI Developers
Hire AI developers who ship real systems — not just demos.
Senior AI and ML engineers with production RAG, LLM, evals, MLOps and agent experience. Full-time on your product, working your hours.
Shipped in USA · Europe · Middle East · Pakistan
SaaSHealthcareFintechEcommerceDeveloper toolsInternal platforms
/ In short
Hire AI developers is a dedicated staffing engagement where vetted senior AI and ML engineers embed in your product team under a monthly retainer. It is designed for ongoing AI roadmap execution, not one-off agency delivery against a fixed scope.
Senior-only
Talent bar
Client-approved
Selection process
Embedded
Working model
Flexible
Scaling terms
/ What this service includes
What we deliver with Hire AI Developers.
01
Dedicated engineer
Full-time AI engineers assigned to your team, roadmap and sprint cadence.
02
Embedded delivery
AI engineers work in your repos, Slack, stand-ups and PR flow with direct day-to-day ownership.
03
Bench continuity
Stable staffing, documented handoff and backup coverage without surprise swaps.
04
Pod when needed
Add design, QA or PM support around the engineer when the roadmap needs a wider pod.
05
Retainer flexibility
Scale up, scale down or pause on agreed monthly terms instead of re-hiring from zero.
/ Is this right for you?
Honest fit check.
A plain answer up front. We'd rather not sell you something you don't need.
✓ Yes if
- You need AI engineers embedded in your team beyond a quick sprint
- You want senior engineers, not a rotating bench of juniors
- You want to interview and approve every engineer yourself
× Not a fit if
- You need an agency to own discovery, roadmap and fixed-scope delivery end to end
- You need a very short gig — hire a freelancer instead
- You want the lowest global rate without quality bar — we're not it
- You won't involve a technical reviewer on your side
/ Deliverables
What you get with Hire AI Developers.
- Senior AI engineer
- Direct interview and approval before start
- Your repos, boards, Slack and stand-up cadence
- Written weekly delivery updates and handoff notes
- Replacement and continuity planning if team shape changes
- Monthly retainer with agreed scaling terms
/ Best fit
Who this service is for.
- Teams with an existing roadmap that need AI engineers embedded quickly
- CTOs and product leads who want direct engineering leverage without a long hiring cycle
- Founders who want interview control and day-to-day visibility inside their own tools
- Teams that need continuity beyond a one-off project sprint
/ Technologies
Our stack, battle-tested.
OpenAIAnthropicLlama 3LangChainLangGraphpgvectorPineconePythonNodeMLflow
/ Comparison
AI developers or project delivery?
If you need...
Better fit
A AI developers embedded in your sprint cadence for ongoing roadmap work
Hire AI developers
A team to own discovery, solution design and delivery against a defined scope
AI development services
Direct interview control and day-to-day engineering management
Hire AI developers
A one-off launch or rebuild with agency-owned QA and release process
AI development services
/ Pricing & timeline
Typical range
Custom quote after scoping
Timeline
Rolling retainer
Team shape
Solo / Pair / Pod (3–5 engineers + 1 PM + 1 designer)
Pricing is quoted after discovery based on scope, team shape and delivery timeline. Monthly retainers are quoted per engineer after role, seniority, overlap needs and expected duration are scoped.
/ Why us
What makes us different.
01
Senior engineers stay on the work
The people you meet in discovery stay involved through architecture, delivery and launch.
02
Search, performance and accessibility are built in
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
03
Architecture is explained in writing
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
04
Your team owns the output
Repos, infra, analytics and documentation live in your accounts from the beginning.
/ Relevant proof
Related case studies for this page.
Real delivery examples tied to this service area, so buyers can move from claims to shipped work.
AI
Arabic RAG chatbot with private deployment
A regulated fintech team needed Arabic retrieval and bilingual answer quality without moving sensitive data to external infrastructure.
Private deployment with bilingual answer quality
AI
LLM integration workbench inside an existing SaaS product
A product team added multiple LLM-powered workflows into an existing SaaS platform with model routing, prompt controls and request-level observability.
Safer in-product AI delivery with clearer cost visibility
/ Client signals
What clients noticed about this kind of work.
USA
“The difference was that Cuibit treated retrieval quality, evals and guardrails as part of the product, not as cleanup after launch. That is why the system earned trust internally.”
AF
Aisha Farooq
Head of Platform · Knowledge operations team
USA
“What we needed was not a demo bot. We needed AI features inside the product with cost visibility and sensible controls, and Cuibit built the layer we could actually operate.”
JP
Jordan Price
Product Lead · Vertical SaaS company
/ Further reading
Related insights and buying guides.
Supporting articles that help buyers understand the tradeoffs, architecture choices and implementation details behind this service area.
AI Systems
What Makes Content Retrieval-Ready for AI Systems
Retrieval-ready content is structured, specific, self-contained, and easy for search systems, RAG pipelines, and LLM tools to extract accurately.
Read insight →
AI Development
What RAG Development Actually Includes
RAG development is more than connecting documents to a chatbot. It includes content preparation, retrieval design, evaluation, security, UX, and maintenance.
Read insight →
AI / Pricing Guide
AI Chatbot Development Cost in 2026: Pricing, Architecture, and What Businesses Should Expect
A practical May 2026 guide to AI chatbot development cost covering pricing ranges, RAG, LLM integration, workflow automation, hidden costs, and what businesses should actually budget for.
Read insight →
/ FAQ
Frequently asked questions
Every AI engineer is senior-level, interviewed on RAG architecture, LLM integration, evals, prompt engineering and production ML — proven in shipped AI systems, not just demos.
We can usually share shortlisted profiles quickly and start once interviews, scope and availability align.
Monthly retainer per engineer, all-in. Pricing is quoted after discovery based on role depth, model stack and expected duration — transparent rate cards shared in the first call.
You do. All code, IP, models and design assets are in your repos and accounts from day one.
Yes — our AI engineers have built production RAG systems with hybrid retrieval, reranking, golden-set evals and observability. Not just prototypes.
OpenAI (GPT-4o, GPT-5), Anthropic (Claude), Google (Gemini), and open-source models (Llama 3, Mistral). Our engineers work with whichever model fits your use case.
Yes — eval pipelines, golden sets, regression tests, guardrails and observability are standard practice for our AI team. Quality is measured, not guessed.
Then our AI Development Services model is usually the better fit. That engagement is for solution design, implementation and launch ownership against a defined scope, not team extension.
/ Explore more
Related services.
/ Next step
Need AI engineers embedded in your existing product team?
We place senior AI engineers into your backlog for ongoing RAG, LLM, eval and MLOps work, with direct interview control and clear monthly retainer terms.
Accepting projects
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