Inventiple builds Model Context Protocol (MCP) servers and agentic AI systems for B2B SaaS companies and enterprise teams. Real guardrails, real observability, real cost controls — not another demo that breaks the moment it touches your production data. Senior engineers only, fixed price, predictable timelines.
LLMs are extraordinary at language. They are also, by default, completely disconnected from your real systems — your customer database, your internal APIs, your domain knowledge, your tools. To actually deliver value to a customer or an internal user, an LLM has to act on real data and trigger real actions. That gap, between "powerful language model" and "useful product feature," is where most agentic AI initiatives die.
Demos work. Production doesn't. A LangChain notebook with a working agent on a conference projector is a different animal from a customer-facing agent at 2 AM on a Tuesday with 50 concurrent users, malformed inputs, and a flaky third-party API. We've inherited a dozen projects in 2025 where the team got 80% of the way there and stalled — not because the model was bad, but because hallucination, error handling, observability, cost control, and security were afterthoughts.
Custom integrations don't compose. Teams write a one-off integration for Claude. Then they want to try GPT-5, so they rewrite it. Then Cursor wants to use the same tools, so they fork it again. Within six months you have three nearly-identical, slightly-different integration layers, all subtly broken in different ways. This is the problem MCP was designed to solve, and almost nobody is building MCP servers correctly.
Cost runaway and infinite loops are silent killers. The most common production incidents we've seen with agentic systems built by inexperienced teams: agents that retry indefinitely on a single failed tool call, agents that recursively call themselves through poorly structured handoffs, and agents that quietly burn $4,000 in a weekend because nobody set a budget cap. None of these are model problems. All of them are architecture problems.
Security is usually an afterthought. An agent with broad tool access is, in effect, a new privileged user inside your system. Most agentic implementations we audit have no per-tool authorization, no audit logging, no input sanitization, and no way to revoke access cleanly. This is acceptable for a prototype. It is malpractice for production.
The result, for most teams: months of work, a product feature that works in a demo but not in production, and a slow walk back to the planning phase. We exist because building agentic AI correctly is a specialized skill, and very few teams have shipped enough of these systems to know where the real failure modes are.
Our MCP and agentic AI work is structured around one rule: every system we ship has to survive contact with real users, real data, and real failure conditions on day one. That rule shapes the stack, the architecture, and the engineering process.
For any project that needs an LLM to interact with your real systems, we build the integration as an MCP server. Build it once, use it from Claude, GPT, Cursor, custom agents, internal copilots, and anything else MCP-compatible without rewriting. Your tools and data become a typed, audited, versioned interface — not a fragile script.
Before we write a single agent prompt, we set the budget caps, step limits, allowlisted tools, and input/output schemas. Then we write the evaluation harness — the regression tests that prove the agent behaves correctly on a known set of cases. Only then do we start improving capability. This inversion is uncomfortable for clients used to capability-first work; it is the single biggest reason our production systems don't break.
Every agent decision, every tool call, every model token is traced and logged from day one. You can see in real time which prompts cost what, which agents loop on which inputs, and where users drop off. We use Helicone, LangFuse, or Braintrust for this, integrated with whatever existing monitoring you have (Datadog, Grafana, internal). When something breaks in production, you can find the root cause in minutes, not days.
We architect every system so the LLM provider is a swap, not a rewrite. Today it might be Claude; in six months, when GPT-6 or a new open-source model shifts the price-performance curve, you swap it without re-architecting the agent. This matters in 2026 because the model layer is moving fast and provider lock-in is a strategic liability.
Every engineer on your project has 7+ years of production experience. For MCP and agentic work specifically, every engineer has personally shipped at least one production agentic system before touching yours. We don't learn on your project. We don't have a junior bench. You meet your team in discovery and they stay until launch.
Unlike traditional MVP work, MCP and agentic AI engagements vary widely in scope. Here are the three engagement shapes we run, with realistic timelines and what's actually delivered.
One MCP server exposing one data source or system (e.g., your Postgres database, your CRM, your internal API). Includes typed tool definitions, auth scoping, audit logging, rate limiting, and full test coverage. Deployed inside your cloud. Best for: teams that already have an LLM strategy and need clean, reusable integration to internal systems.
A 2-to-3-agent system targeting a single business domain (customer support, sales research, internal ops, code review). Includes MCP server(s) for required tool access, agent orchestration with LangGraph or equivalent, evaluation harness with regression tests, full observability, budget and step caps, and admin tooling. Deployed and integrated with your product or internal tools. Best for: B2B SaaS adding an AI feature to their product or an internal team replacing repetitive knowledge work.
Multi-agent platform with multiple MCP integrations, role-based access control, audit-ready compliance scaffolding (SOC 2 or HIPAA), human-in-the-loop approval flows for high-risk actions, multi-tenancy, and admin dashboards for governance and cost monitoring. Best for: enterprises rolling out agentic AI across multiple business units, or B2B SaaS shipping AI as a major product line.
For all three, the first week is a paid discovery — typically $5,000–$10,000, credited against the final price if you proceed. We won't quote a project without it because accurately scoping agentic work requires understanding your actual systems, not just your stated requirements.
Below are the price ranges for the three engagement types above. We quote fixed prices after discovery — never hourly. If we underestimate, we eat the cost; that's what aligned incentives look like.
Single integration, scoped tools, internal use.
Production AI feature embedded in your product or internal ops.
Multi-domain agents, compliance, governance.
Payment terms match our MVP work: 40% on kickoff, 30% at mid-engagement demo, 30% on production launch. Discovery (1 week, $5K–$10K) is billed separately and credited against the project price if you proceed.
Provider-agnostic by design. We pick frameworks for their fit to your problem, not because of a vendor relationship. Here's what we ship most often in 2026.
MCP (Model Context Protocol) is Anthropic's open standard for connecting large language models to your real systems — databases, APIs, file stores, internal services, and SaaS tools. An MCP server is a small program that exposes a set of those tools or data sources to any MCP-compatible LLM in a safe, structured, auditable way. You need one if you want AI features that actually use your live data instead of stale embeddings, or if you want to give an agent tool-use without writing custom integrations for every model your team experiments with. Build the MCP server once, and Claude, GPT, Cursor, and any future MCP client can use it.
Three reasons. First, MCP is a standard — your investment isn't locked to one model provider. Second, MCP servers force a clean security boundary: you decide exactly which tools, which data, and which actions an LLM can take, with full audit logs of every call. Third, MCP composes. A well-built MCP server can be reused across your internal AI assistants, customer-facing agents, and developer tooling without rewriting the integration layer. Custom per-model integrations break when models change, don't compose, and become technical debt within 6 months.
Production systems, not demos. Specifically: customer-facing AI copilots embedded in B2B SaaS products, multi-step automation agents that handle research, drafting, and outreach workflows, internal developer-tools agents (code review bots, doc generators, on-call triage assistants), domain-specific agents in finance, healthcare, legal, and operations, and AI orchestration layers that route tasks across multiple specialized agents. Roughly 70% of our 2026 agent work uses LangGraph or custom orchestration; 30% uses CrewAI, AutoGen, or vendor-specific frameworks where the client has a preference.
Every agentic system we ship has four hardened layers: (1) typed tool interfaces with strict input and output schemas that fail fast, (2) per-agent budget caps and step limits enforced before any LLM call, (3) an evaluation harness (Braintrust or LangSmith) that runs regression tests on every prompt or tool change, and (4) full observability via Helicone or LangFuse so you can trace every decision, every tool call, and every dollar spent in real time. We've never had a production agent hit an unbounded loop or blow a budget — because the architecture makes both impossible.
Yes. The most common MCP servers we build expose: PostgreSQL, MySQL, MongoDB, BigQuery, and Snowflake databases (read-only or scoped writes), Salesforce, HubSpot, and other CRMs, internal REST and GraphQL APIs behind your auth, S3 and other object stores, and SaaS tools like Slack, Notion, Linear, GitHub, and Google Workspace. For internal-only systems, we build MCP servers that run inside your VPC and never expose data externally.
MCP servers we ship include: per-tool authentication and authorization scoping, structured audit logs of every tool call (who, what, when, result), input sanitization and output redaction for PII and secrets, rate limiting and quota enforcement, and optional approval gates for high-risk actions (human-in-the-loop). For regulated environments — HIPAA, SOC 2, GDPR — we deploy inside your cloud account with no data egress to third parties, and we sign BAAs where required.
Provider-agnostic by design. We routinely ship with Claude (Anthropic), GPT-5 and o-series (OpenAI), Gemini (Google), and open-source models via vLLM or Together. For orchestration: LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, and Anthropic's MCP SDKs in TypeScript and Python. For evaluation: Braintrust, LangSmith, or our own internal harness. We pick the right tool for the job — we don't have a vendor relationship that biases the recommendation.
Yes. We maintain internal MCP server templates for the most common integration patterns — Postgres, REST APIs with OAuth, S3, and Slack — that we use as a starting point on most engagements. This typically shaves 1–2 weeks off the build. We can also contribute back open-source MCP servers where appropriate; we've published reference implementations for several integration patterns.
A single-purpose MCP server (one data source, scoped tools, basic auth) typically takes 2–3 weeks. A production agentic system with 2–3 agents, custom orchestration, evaluation harness, and observability typically takes 6–10 weeks. A larger multi-agent platform with multiple integrations, compliance scaffolding, and admin tooling typically takes 10–14 weeks. We give you a fixed timeline and fixed price after a 1-week paid discovery, not before — accurate scoping requires actually understanding your systems.
MCP is open-source and increasingly multi-vendor — OpenAI, Cursor, Google, and a growing list of clients now support it natively. The protocol is governed in a way similar to language server protocol (LSP), which has remained stable for nearly a decade. Even in a worst case where MCP forks, your MCP server is still just a typed API server with audit logging and auth — the same code translates trivially to whatever replaces it. We architect every server so the MCP wire format is a thin layer over a well-typed internal API; swapping the layer is a 1–2 day job, not a rewrite.