WHY MIGHTYBOT
Building a production-grade AI agent platform takes 5-8 engineers and 12-18 months. MightyBot delivers in weeks. Here is the reality.
Why MightyBot
You could try to build this. Production-grade policy automation requires 7 layers of infrastructure, 5-8 engineers, and 12-18 months. Gartner predicts 40% of agentic AI projects will fail to reach production by 2027. We have already built it. You get results in weeks.
Every engineering team has the same instinct: we can build this ourselves. A proof of concept comes together in weeks. Then production happens, and the distance between demo and production is a canyon.
THE BUILD
Versioning, plain-English configuration, multi-tenant isolation, rollback. Instant retrieval of which policy version governed any decision.
Classification, extraction, confidence routing across rotated pages, handwritten annotations, inconsistent layouts, and multi-entity packages.
Parallel agent orchestration, not sequential prompt chains. Execution graphs where independent analyses run simultaneously.
Embeddings, hybrid retrieval, RAG pipelines, and agent memory across long-running workflows.
Decision traces and complete why-trails linking each decision to policy, evidence, and sign-off.
Backtesting, feedback loops, sandbox environments, and regression suites validating behavior across model upgrades.
Audit trails, review gates, pause and resume, regulatory reporting, confidence thresholds, and human escalation.
THE MATH
And you still will not have solved the hard problems. Gartner predicts 40% of agentic AI projects will fail to reach production by 2027, not because teams are weak, but because the gap between demo and production is consistently underestimated.
Language models change quarterly. Each upgrade subtly alters behavior. MightyBot manages business rules as versioned policies and regression-tests every evaluation across model upgrades before production. You maintain code. We maintain a platform.
Internal builds are tightly coupled to the first workflow. When the second use case arrives, most infrastructure needs rebuilding. MightyBot separates concerns, so adding a use case means authoring policies, not re-engineering the platform.
INTEGRATION
| Cost Category | Year 1 | Years 2-3 (Annual) |
|---|---|---|
| Engineering team (5-8 senior) | $1M - $2M | $800K - $1.5M |
| Cloud AI services | $100K - $300K | $150K - $400K |
| Infrastructure | $50K - $150K | $75K - $200K |
| Security and compliance | $150K - $300K | $50K - $100K |
| Opportunity cost (12-18 mo delay) | Significant | - |
| Total | $1.3M - $2.75M | $1.1M - $2.2M |
Three-year TCO: $3.5M - $7.15M for a single workflow.
| Category | Detail |
|---|---|
| Time to production | 30 days |
| Implementation | Included in onboarding |
| Ongoing policy management | Internal team hours, not FTEs |
| Additional workflows | Policy authoring, not re-engineering |
INTEGRATION
One scenario: requirements outside any existing platform, 18+ months before production is needed, a permanent team of senior AI engineers, and willingness to own the model lifecycle indefinitely. For every other scenario, the build decision is a trap.
FAQ
Weeks versus 12-18 months. MightyBot's deployment covers policy configuration, system integration, document pipeline setup, and production go-live. An internal build requires 5-8 senior engineers for over a year, assuming no architectural pivots.
Yes, but the longer you build, the harder the switch. Deploy MightyBot on your first workflow, then use production results to benchmark against the internal plan. The data tends to make the decision obvious.
Unique requirements are usually unique policies, not unique infrastructure. Institution-specific rules are authored in plain English while the execution, audit, and document infrastructure stays the same.
No. Your team controls policies, thresholds, escalation rules, and integration configuration. MightyBot controls the execution infrastructure so you do not have to rebuild it for every workflow.
An evaluation system validates agent behavior across model upgrades before anything reaches production. Every policy evaluation is regression-tested and every document pipeline is revalidated.
