Your AI engineering team.
Without the hiring timeline.
A named team of senior AI engineers embedded in your product — shipping every sprint, knowing your codebase, and available without the 6-month recruitment process or the $400K fully-loaded salary.
Currently supporting: VetERP Phase 2 · QReward · Resay · Qualia Proflow Global
Not advisors. Engineers who ship.
The fractional model has a reputation problem. Most fractional engagements promise embedded expertise and deliver part-time advisory — a call or two a week, a roadmap review, some architecture suggestions. The code doesn’t move. The product doesn’t ship. You pay for a senior perspective and get a consultant who’s not quite accountable.
That’s not what this is.
Kharaayo’s Fractional AI Team is an embedded engineering function. You get named engineers — people who know your codebase, attend your standups if useful, work in your ticket system, and ship in two-week sprints. Every month ends with something in production, not a status update.
The model works because of three commitments we hold to every retainer:
Named engineers, not anonymous capacity. You know exactly who is working on your product. The same engineers show up every sprint, building context over time — and that accumulated context is what makes the output fast.
Sprint-based delivery, not hours-billed. Every sprint has a written scope. At the end there is a working build and a written summary. You see progress in code, not in hour logs.
Your codebase, your ownership. Everything built during the retainer belongs to you. No lock-in, no retained ownership. When you’re ready to hire in-house, we help you transition.
What the team builds.
The Fractional AI Team is specialized in AI-native and AI-integrated product engineering — an AI-focused engineering function for companies whose products live at the intersection of software and intelligence.
AI Feature Development
Building, iterating, and maintaining AI-powered features inside your product — intelligent search, recommendation engines, natural language interfaces, document processing, summarization, generation — in production, not prototype.
Agent Development & Maintenance
Building and maintaining AI agents and multi-agent components: task-specific agents, RAG-powered knowledge agents, autonomous workflow components, and the orchestration logic that connects them — plus ongoing model updates and prompt tuning.
Backend & API Engineering
FastAPI, Node.js, PostgreSQL, Redis — the backend layer that makes AI features fast, reliable, and production-ready. Async task queues, data pipelines, LLM API management, rate limiting, cost controls, and caching.
Infrastructure & Deployment
Kubernetes, Docker, CI/CD pipelines, cloud infrastructure (AWS/GCP), monitoring and observability. The team manages the deployment layer so your product stays up, scales when it needs to, and alerts when something breaks.
Frontend Engineering
Next.js 15, React 19, TypeScript. Building the interfaces your users see — dashboards, AI-powered UIs, and the design-to-code execution that turns Studio concepts into production interfaces.
Technical Architecture & Roadmap Input
As engineers embedded in your product, the team brings informed architectural opinions to roadmap conversations. Not advisory-only — the same engineers who write the code also advise on what to build next. Accountability is built in.
The team’s focus is always determined by your product roadmap. At the start of each retainer month, sprint scope is agreed from your priority list — not from what the team finds interesting.
Who you get.
Every Fractional AI Team engagement is staffed with named engineers. You know who is working on your product before the retainer starts, and the composition is agreed upfront.
One Senior AI Engineer
Full-stack AI engineering capability: agent development, LLM integration, backend, and deployment. Appropriate for products that need consistent, focused AI engineering in a defined scope.
~40–80 hrs/moSenior AI Engineer + Engineer
The senior engineer holds the architecture and AI layer. The supporting engineer handles frontend, backend services, and infrastructure. Combined, full-stack output.
~80–160 hrs/moSenior AI Engineer + Engineer + Specialist
For products with high throughput requirements, a third member — a specialist (data engineering, DevOps, mobile, or a second AI/ML engineer). Scope and composition defined at the Team Fit Call.
160+ hrs/moNamed engagement
When you start a retainer, you receive:
- The name, role, and background of each engineer on your team
- A brief introduction session before the first sprint begins
- Direct communication access (Slack, email, your chosen channel — not a ticketing system you can’t reach a human through)
There is no anonymous bench. The people on your retainer are the people doing the work.
Transparent terms. No surprises.
The retainer model is straightforward. Here is exactly what you’re buying, what it costs, and what the terms are.
Monthly retainer, sprint-based delivery.
Work runs in two-week sprints. Each sprint has a written scope, a weekly check-in, and a working demo or deployment at the close. You see the work as it forms — not in a monthly status email.
Fixed monthly fee.
The retainer is a fixed monthly fee for a defined team composition and output scope. You know the number before the first month starts and it doesn’t change without a written agreement.
3-month minimum commitment.
We ask for a 3-month minimum so the team can build meaningful context on your product. Onboarding takes time — the value compounds over months, not weeks. After that, the retainer continues month-to-month with 30 days’ notice.
No lock-in beyond the minimum.
After the 3-month minimum, the engagement is month-to-month. Scale the team up, down, pause, or end it with 30 days’ notice. There are no exit fees.
Indicative pricing
[PLACEHOLDER — confirm pricing tiers with Daniel before launch. Market reference: embedded fractional AI engineering teams range from $8,000–$20,000/month for a senior engineer + supporting engineer at international-grade quality.] Exact pricing is confirmed at the Team Fit Call, based on team composition and scope.
What’s included
- Named engineering team with agreed composition
- Two-week sprint cadence with written scopes and summaries
- Weekly async check-in (written or short video — your choice)
- Access to the team via direct channel (Slack, email, your tool)
- All code committed to your repository — full ownership, no retention
- Monthly retainer review: scope, velocity, and what’s next
What’s not included
- Cloud infrastructure costs (billed directly to your accounts at cost)
- Third-party API fees (LLM, data providers — billed at cost)
- Design work (available as an add-on through Kharaayo Studio — see Creative Retainer)
From fit call to first sprint in two weeks.
Team Fit Call30 minutes, free
Before any retainer is agreed, we schedule a 30-minute Team Fit Call. We want to understand your product, roadmap, and team’s current state. You want to understand who the engineers are and whether the model is right for your stage.
If it’s a fit, we propose a team composition and a first-sprint scope. If it’s not, we say so — and often point you toward the right service.
Onboarding WeekWeek 1
The first week is orientation: repository access, codebase review, architecture walkthrough with your team, tooling setup, and the first sprint scope agreed in writing. No code is written in Week 1 — context is built. The team that ships in Week 2 understands what they’re shipping into.
Sprint 1Weeks 2–3
First production sprint. Written scope at the start. Working build at the end. Sprint summary at close — what shipped, what changed, what carries to Sprint 2.
Rhythm EstablishedMonth 2 onwards
By the second month, the team has product context, repository familiarity, and an established sprint rhythm. Velocity increases as context deepens — this is where the retainer model earns its value.
This retainer is right for you if:
Right fit
- You have a product in production and a growing roadmap. Not starting from scratch — you have something live and a backlog of things to build.
- You need consistent, ongoing engineering output — not a one-time build. Monthly sprints delivering production features, not a project with a handoff date.
- You can’t yet justify a full-time senior AI engineer or team. Either the stage isn’t there, the budget doesn’t reach a full-time hire, or the right candidate doesn’t exist in your geography.
- You want the same engineers every sprint. Named people, accumulated context, a relationship that improves over time — not anonymous capacity that rotates every month.
- You’re AI-focused or AI-adjacent. Your product has AI components now, or it needs them. The team’s specialty is AI-native engineering.
Not the right fit
- You need a product built from scratch. That’s an AI-Native MVP engagement. Fractional is for products that already exist and need to keep growing.
- You need a single short sprint to ship one feature. The 3-month minimum exists because it takes a full cycle to build the context that makes the team productive. Better to run a scoped AI Integration project.
- You want advisory without accountability for shipping. The Fractional AI Team writes and ships code. If you need strategy without production responsibility, that’s a different engagement — and we’ll tell you.
- You need a 40-person team. We are not a staffing agency. The Fractional AI Team is a specialist unit of 1–4 senior engineers. For large-scale team builds, we can make introductions.
Need a new product built first? See AI-Native MVP. AI-Native MVP →
Need a defined AI integration project rather than an ongoing team? See AI Integration & Automation. AI Integration & Automation →
Need ongoing creative and design output alongside engineering? See Creative Retainer. Creative Retainer →
Products we’re building with right now.
A selection of active and recent retainer engagements — ongoing engineering partnerships, not completed projects.
Unlike our other proof sections, this one intentionally shows ongoing work, not completed case studies. These are live relationships, not archives.
VetERP
Veterinary Practice Management System — Ongoing Phase 2
Following MVP approval, Kharaayo Tech continues as the engineering partner for Phase 2 — delivering consistent sprint-cadence output with CTO-level architectural involvement on escalations. [PLACEHOLDER — confirm client approval + Phase 2 specifics.]
QReward
Active client engagement
[PLACEHOLDER — one-line description of QReward and the ongoing relationship; confirm client approval.] Active sprint cycle advancing with zero missed commitments — a high-sensitivity client maintained through consistent reliability.
Qualia Proflow Global
SaaS Platform — Ongoing Maintenance & Integration Support
Standing SLA-level maintenance and integration support for a live SaaS platform. Zero outstanding issues at month-end standard, maintained consistently. [PLACEHOLDER — confirm client approval.]
Resay
Active client engagement
[PLACEHOLDER — one-line description of Resay and the engineering engagement; confirm client approval.] Sprint-cycle development underway from first sprint kickoff.