You define the business outcomes. Kovil AI handles everything else: team composition, architecture, delivery, talent management, and monthly reporting. No AI lead required on your side. No daily engineering management. Just a working AI function and a monthly review that makes sense to a CEO.
The standard advice is: hire a VP of AI or a CTO, then build a team under them. But this advice assumes you can accurately evaluate AI talent, know what good looks like in an AI architecture, and have the patience for a 6-12 month ramp before anything ships.
Most businesses cannot afford that ramp. They have a commercial opportunity now. They need AI to be working and compounding in months, not years.
Managed AI engineering is the answer: Kovil AI acts as your AI function. We own the team, the architecture, the delivery, and the reporting. You stay focused on the product and the business while AI becomes a real, measurable capability underneath it.
Six management responsibilities typically spread across a VP of AI, a Tech Lead, an HR partner, and a project manager. We handle all of them.
Quarterly sessions to define what AI should deliver for your business. We translate commercial goals into a technical roadmap with measurable milestones.
We assemble and manage the team. If a specialism is needed, we add it. If a role is no longer needed, we scale it back. No recruitment overhead for you.
Our AI Lead owns the technical architecture. You get recommendations and rationale, not a stack of options to decide between without enough context.
Bi-weekly sprints. Milestone tracking against the agreed roadmap. You see what shipped and what is next, not detailed technical progress that requires interpretation.
A structured monthly report: OKR progress, decisions made, blockers resolved, next 30 days plan. Designed to be useful to a CEO or CPO, not just a CTO.
Everything documented as we go: architecture decisions, model evaluation frameworks, deployment playbooks. If you ever hire in-house, you are ready.
The right model depends on how much AI management capacity you have internally and how fast you need to move.
| Dimension | Managed AI Engineering | Dedicated AI Team | Hire In-House |
|---|---|---|---|
| Who manages the team | Kovil AI | You (with Tech Lead support) | You (with hired leads) |
| Technical decisions | Kovil AI (with your approval on major arcs) | Shared: Tech Lead proposes, you approve | Internal team |
| Reporting format | Monthly Business Review (OKR-based) | Weekly demo + sprint board access | Internal meetings and reports |
| Talent management | Kovil AI swaps / scales the team | Kovil AI sources, you direct | Full internal HR overhead |
| Time to operating | 4-6 weeks (includes strategy phase) | 2-3 weeks | 6-12 months (sequential hiring) |
| Best for | No AI lead; want outcomes not daily management | Have product context; want to direct engineers | Building permanent AI function long-term |
Month by month, from scoping to sustained delivery.
Technical discovery session, AI roadmap defined, OKRs agreed, team composed.
Team operational, architecture documented, first sprint shipped, CI/CD live.
First MBR: OKR progress, decisions made, next 30 days plan. Cadence established.
Monthly MBRs, quarterly roadmap reviews, team scales with the product phase.
Tell us what you want AI to deliver for your business. We will handle the engineering function that gets you there.
Start the Discovery CallA PE-backed B2B SaaS company had 120 employees, a strong commercial team, and no AI engineering capability. The board had set an OKR: deliver AI-powered features in Q1 to protect the product's competitive position. We ran a discovery sprint in month 0, composed a 4-person managed AI team (AI Lead, 2 LLM engineers, 1 MLOps engineer), and defined 3 OKRs. By end of Q1 the team had shipped: AI document summarisation, an intelligent contract comparison tool, and a smart activity feed with LLM-powered recommendations. All three went live on schedule.
Managed AI engineering is a model where an external provider owns the AI engineering function end-to-end. Kovil AI handles team composition, technical architecture, sprint delivery, talent management, and monthly reporting. You define the business outcomes you want and we deliver them, without you needing to manage engineers, track technical progress daily, or make AI hiring decisions.
Managed AI engineering is typically suited for: companies that want serious AI capability but do not yet have an AI lead or CTO who can manage an AI team; PE-backed and VC-backed businesses where the leadership team is commercially focused and does not want to build deep technical management overhead; and enterprises running AI as a product initiative where the internal team will eventually absorb the function, but needs external delivery capability in the interim.
We own: technical architecture and AI stack decisions, team composition and any talent swaps, sprint planning and delivery milestones, code quality and model performance standards, AI governance and evaluation frameworks, and a monthly business review with OKR tracking. You receive working software, documented architecture, and a monthly report, not a stack of engineer updates to interpret.
With a dedicated AI team, you manage the team: you set sprint priorities, approve technical decisions, and manage escalations. Kovil AI provides the engineers and the Tech Lead. With managed AI engineering, we manage everything: you agree the OKRs at the start of each quarter and Kovil AI is accountable for delivery. The difference is the management layer, dedicated is staff augmentation plus a tech lead, managed is a fully outsourced AI function.
We have delivered under managed models: LLM-powered products (RAG pipelines, chat interfaces, document intelligence), AI agent workflows (multi-agent orchestration, autonomous task execution), ML systems (predictive models, recommendation engines, anomaly detection), and AI-integrated product features (intelligent search, AI-generated content, smart automation). The managed model is capability-agnostic, we compose the team to match the technical scope.
Month 0 is a discovery and scoping sprint: we conduct a technical discovery session, agree on the AI roadmap and OKRs, and compose the team. By the end of month 1, the team is operational, the architecture is documented, and the first sprint has shipped. Monthly business reviews begin in month 2.
Monthly Business Review (MBR): a structured report covering OKR progress, milestones delivered vs planned, technical decisions made and why, blockers resolved, and the plan for the next 30 days. Quarterly: a full AI function review with updated roadmap, team performance, and strategic recommendations. Real-time: async Slack updates and access to the sprint board in your tool.
Yes, and many clients plan for this from the start. We document everything as we go: architecture decisions, model evaluation frameworks, deployment playbooks, and codebase standards. When you are ready to hire in-house, we provide a formal handover programme: documentation, a knowledge transfer period with your new team, and ongoing advisory support if needed. Kovil AI is not designed to be a permanent lock-in.
Kovil AI runs your AI function. You focus on the business. We handle everything else.
Start the Conversation