From first conversation to production handover. Kovil AI runs the full project: scoping, architecture, build, evaluation infrastructure, deployment, and knowledge transfer. You own everything we build.
85% of enterprise AI projects fail to reach production. The reasons are almost never the model. They are the process: vague requirements, no evaluation infrastructure, poor data quality discovered mid-build, and no one accountable for the end-to-end outcome.
Kovil AI was designed to fix this. Every project starts with a 2-week discovery sprint that produces a technical spec, data audit, risk log, and architecture diagram. We build the evaluation harness on day one of the build phase, not as an afterthought. And we stay accountable through to production deployment, not just code delivery.
The result is AI projects that ship on time, meet their quality targets, and continue to perform after handover.
Four phases with clear deliverables at each gate. No handwave moments, no "it will work itself out."
Discovery
Wk 1-2
Foundation
Wk 3-5
Core Build
Wk 6-9
Production
Wk 10-12
Six core AI project types Kovil AI has shipped to production. Every one includes eval infrastructure, monitoring, and full handover.
Customer support, internal helpdesk, product assistant. Multi-turn conversation, memory, escalation, analytics dashboard. Deployed to web, Slack, or WhatsApp.
Contract analysis, invoice extraction, regulatory review, report summarisation. Structured output with citation links. Human-review workflow for flagged documents.
Internal knowledge base, technical docs assistant, research Q&A. Hybrid search, re-ranking, RAGAS evaluation, source citations. Built to stay accurate as your docs change.
Multi-step task automation: browse, write code, query APIs, complete workflows. Human-in-the-loop for high-stakes actions. Stateful LangGraph architecture for reliability.
Add AI features to your existing SaaS product: summarise, classify, generate, extract. Streaming, token budgets, fallback chains, and prompt versioning from day one.
Domain-specific model for medical coding, legal clause classification, financial document parsing. Requires labelled training data. Higher accuracy at lower inference cost.
Tell us the business problem. We will recommend the right AI approach, technology stack, and engagement model in a free 30-minute scoping call.
Book a Free Scoping CallThe right model depends on how well-defined your requirements are and how much budget risk you want to carry.
Know exactly what you are paying before a line of code is written.
Advantages
Consider if
Pay when the metric moves. Kovil AI takes shared risk on performance.
Advantages
Consider if
Maximum flexibility. Change priorities sprint-to-sprint.
Advantages
Consider if
A Series B lending startup needed an AI underwriting assistant: ingest applicant documents, extract financial data, cross-reference against internal risk models, and generate a structured decision recommendation for human underwriters.
Discovery sprint uncovered that their applicant PDFs had 14 distinct layouts requiring adaptive parsing. We added a document classifier as a pre-processing step (not in original scope), priced it in the fixed-price proposal, and delivered the full system in 11 weeks.
A full AI project development engagement with Kovil AI includes: a discovery sprint (2 weeks, producing technical spec and architecture), build phase (4-12 weeks depending on scope), evaluation infrastructure (eval suite, RAGAS scoring, regression tests), production deployment, monitoring setup, documentation, and a knowledge transfer session. You own all code, prompts, eval datasets, and model weights at handover.
Total timeline depends on scope. A focused AI chatbot integration: 6-8 weeks. A RAG pipeline with evaluation: 8-12 weeks. A multi-agent workflow system: 12-16 weeks. These timelines include the 2-week discovery sprint. The discovery sprint is not optional — it is what makes the rest of the project predictable.
Fixed-price is best when your requirements are clear and stable — good for MVP builds, defined integrations, and document processing systems. Time-and-materials is best when requirements may evolve — good for exploratory work or when you want to stay flexible on priorities. Outcome-based is best when you have a clear KPI and want Kovil AI to share the performance risk. We will recommend the right model after understanding your project in a scoping call.
Yes. We integrate with your existing stack, not around it. We have shipped AI systems on top of AWS, Azure, GCP, Vercel, and on-premise infrastructure. Common integrations include Salesforce, HubSpot, Zendesk, Slack, Confluence, Notion, Google Drive, SharePoint, and custom REST APIs. If you use it, we have probably integrated with it.
We do a structured handover: documentation review, codebase walkthrough, monitoring and alerting orientation, and a 2-week post-launch support window included in all engagements. After that, you can maintain the system internally, engage us for ongoing retainer support, or bring in your own team — the code and architecture are yours to operate.
Every project gets an evaluation suite from day one. For RAG systems: RAGAS context recall, faithfulness, and answer relevancy. For classification: F1, precision, recall on a held-out test set. For agents: task success rate and step efficiency. For generation tasks: BLEU, ROUGE, or LLM-as-judge depending on the use case. Eval scores are tracked in a dashboard and run on every deployment to catch regressions.
Yes. We frequently work alongside internal teams. Common structures: Kovil AI leads architecture and builds the AI layer while your team handles frontend or integrations; Kovil AI upskills your internal team during the build; or Kovil AI delivers the first version and your team takes over for ongoing development. The structure depends on your team's current capabilities and where you want to own the work long-term.
The top failure modes: (1) undefined success criteria — prevented by our eval-first approach; (2) poor data quality — caught in discovery sprint; (3) scope creep — managed by change order process or T&M structure; (4) hallucination in production — mitigated by RAG grounding, structured output, and confidence gating; (5) no monitoring after launch — prevented by our mandatory monitoring setup at handover. We have built these guardrails from experience across dozens of AI projects.
Related services and engagement models
Describe the problem you want to solve. We will scope it, recommend the right approach, and tell you honestly what it will take to ship.
Scope Your AI Project