There is a 12-week gap between “we want an AI agent” and “the agent ships.” Most companies live in that gap forever. They run pilots that never reach production, they hire agencies that quote three months of “discovery,” they buy a no-code platform that breaks the moment their workflow gets a single edge case.
studiobuildit closes that gap. We build production AI agents, covering single-agent and multi-agent systems that run in your stack, with memory, evals, and monitoring, in two to ten weeks. Not a prototype. Not a demo. The actual agent.
Who this is for
Founders with one expensive workflow. You have a process that costs $30,000 to $200,000 a month in headcount, or a process so painful that the team avoids it. A production AI agent will not replace your company, but it can replace one specific function, completely, today.
VPs of Engineering whose teams should not be building this. Your engineers are talented. They have not, however, spent the last twelve months living inside Claude code traces, LangSmith eval runs, and MCP server internals. We have. You will get the agent faster, more cheaply, and with patterns your team can extend on their own.
Heads of Operations who need a system, not a chatbot. You do not want “AI.” You want fewer tickets in the queue, faster onboarding, and lower cost per call. We build for outcomes, not buzzwords.
What you get
- A production-grade agent running in your cloud, your stack, your auth perimeter, not on someone else’s hosted platform you cannot audit.
- Memory layer (Mem0 or a custom store) so the agent remembers users, conversations, and context across sessions.
- Tool integrations via MCP for every system the agent needs to read or write: CRM, email, billing, internal APIs, whatever you run.
- Evaluation harness with at least 50 golden examples and automated regression on every commit, wired to LangSmith or Langfuse.
- Observability with full traces, token costs, latency, and error rates dashboarded from day one.
- 30 days of post-launch warranty: anything that breaks, we fix; anything that drifts, we tune; anything that surprises you, we document.
How we work on this
Week 1: Discover. A working call, a tool inventory, and a written one-pager covering what the agent does, what it costs to run, and what the success metric looks like. If we do not proceed, you keep the doc.
Weeks 2 to 3: Prototype. An end-to-end thin slice running in your environment with real data. We are not optimizing yet; we are proving the loop closes.
Weeks 4 to 6: Production hardening. Eval suite, error handling, retries, structured outputs, cost controls, monitoring, and security review. The less glamorous half of AI agent development, and the half that determines whether it survives Q2.
Week 7: Ship and train. Deployment, runbook, and handover sessions with your team. After this point your engineers can extend the agent without us.
Tech stack
Model choice is task-specific. Long-context reasoning means Claude Opus 4.7. Cost-sensitive high-volume means Haiku 4.5 or Gemini 3 Flash. Tool-use-heavy agents run well on GPT-5. We benchmark on your data, not on vendor marketing.
For orchestration, the default is LangGraph when the agent has branching state, Mastra when TypeScript is the team’s first language, CrewAI for role-based multi-agent setups, and Pydantic AI for strict-typed single-agent flows. Tool surface comes through MCP wherever possible, because it is the substrate that lets the agent migrate between models without rewrites.
Vector storage is Pinecone for hosted environments and Qdrant for self-hosted. Memory is Mem0 unless your retention requirements push us toward a custom store. Tracing is LangSmith or Langfuse (self-hosted) depending on data residency. Deployment lands on Vercel or AWS Bedrock depending on which side of the compliance line you are on.
When this is the wrong choice
If you do not yet have a clear workflow the agent will own, you do not need an agent. You need a process map. If your data lives in three different SaaS tools, none of which expose an API, the agent will be brittle regardless of who builds it. If your team cannot allocate a single person to be the agent’s product owner, it will rot the moment we hand it over. In any of those cases, we will tell you on the discovery call and we will not proceed. That is worth more than a 12-week engagement that ships nothing.
Pricing
Starter: $4,000 to $12,000 · ₹3.5L to ₹10L. Single agent, 1 to 2 tool integrations, basic eval suite. 2 to 4 weeks. Right for a single workflow you can describe in one paragraph.
Pro: $12,000 to $35,000 · ₹10L to ₹28L. Multi-agent or single agent with 5-plus tools, custom memory, full eval suite, and monitoring. 4 to 7 weeks. Most common engagement.
Enterprise: $35,000 to $80,000 · ₹28L to ₹65L. Multi-agent with custom MCP servers, on-prem deployment, full security review, and SOC2-friendly logging. 7 to 10 weeks. Right when the agent is mission-critical.
FAQ
How is this different from buying a hosted agent platform? Hosted platforms work well until you need a custom tool, custom memory, custom eval logic, or want to swap models. At that point you are paying enterprise SaaS pricing to be locked in. We build code you own.
Do you provide the model API keys? No, they live in your account, billed to you. That keeps your data inside your perimeter and your costs visible.
What if Anthropic releases a new model mid-build? We design model-agnostic from day one. Swapping a model is a config change and a re-run of evals, not a rebuild.
Can you integrate with our internal APIs? Yes. Most builds include a custom MCP server that wraps your internal systems, which is the cleanest abstraction we have found for AI agent development.
What about hallucinations and accuracy? That is what the eval suite is for. We define accuracy targets up front, for example 95% on the 50 golden examples, measure them in CI, and will not ship until we hit them.
Can my team maintain this after you leave? Yes, that is the explicit handover goal. Every build ships with a runbook, eval scripts, and a two-hour pairing session with your engineers. Most teams are independent by day 30.