MightyBot vs Poetic AI

Poetic reached an eight-figure run rate in 2025 with four employees and a forward-deployed engineering model. Its founder describes hiring forward-deployed engineers from places like Palantir, Retool, and Scale, and frames the core work as what Poetic calls 'the great migration': moving the tens of thousands of undocumented rules that live in operators' heads into a form AI can run, then changing how the business organizes around it. That is powerful for a Fortune 500 that wants results without staffing the build, but the automation is something Poetic builds and operates for you. MightyBot is a platform your team runs. You author policies in the Policy Authoring Studio, start from a growing policy library, and drive deployment yourself in about 60 days. One model migrates your business with a deployed team. The other gives you a system your own people own and run.

Both let operators describe processes in plain English. The difference is what that English becomes. Poetic compiles it into a purpose-built, proprietary language that runs under the hood. You describe the procedure; Poetic owns the artifact. MightyBot's policies are the artifact. They stay in plain English, versioned in git, backtested against historical data, deployed same-day, and rolled back if outcomes drift. When a regulator asks why a decision was made, you point to a policy version a human can read, not a black box only the vendor can inspect.

This is contractual, not just architectural. MightyBot's Terms of Service affirm that you own your policies and business rules, and the platform keeps them as human-readable, versioned files you can export and take with you. Poetic's public Terms of Use (captured June 2026) reserve all platform intellectual property to Poetic and do not state that customers own the automations created on the service; on termination, the customer must cease use and return Poetic's materials, with no stated export right for the automation logic, which lives in Poetic's proprietary language. If ownership and continuity matter, read both companies' terms before you sign.

Poetic ingests videos, operating procedures, and historical examples to learn a procedure at design time, then compiles it to code that operates your existing applications. Its self-healing kicks in when those systems drift: in the founder's own examples, a column renamed from 'month' to 'months,' or a save button that moves. That is automation of app operation, repaired by AI when the UI or schema changes. MightyBot solves a different problem: a runtime document intelligence pipeline that classifies, splits, extracts, normalizes, and reconciles messy multi-format packets (PDFs, scans, images, API payloads) every time a workflow runs, then links each extracted field to the exact page and character offset it came from. Recovering from a moved button is not the same as reading a new 300-page loan file correctly on every execution. The same pipeline treats photos and video as first-class evidence: it detects what an inspection photo shows, checks it against the written report, and links the image back to the file. Poetic ingests video at design time to learn a process; MightyBot reads photos and video as evidence at run time.

Poetic's public results are transaction-data workflows: fraud investigations and KYC at SoFi, dispute handling, and insurance processes at AIG. Those run on structured signals and system data. MightyBot's production footprint is document-packet workflows: loan underwriting, construction draw reviews, and claims, where the hard part is reading inconsistent paperwork, reconciling it against policy, and proving the result. At Built Technologies, MightyBot processes $100B+ in lending activity and cut time on task by about 95%. Pick the platform whose proven domain matches your hardest work.

Poetic's founder compares its improvement loop to data labeling: experts watch the generated procedure run and correct it, for example noting that a threshold is actually a million, not ten thousand, and those corrections merge back into the plain-English source. It is human-in-the-loop tuning by your best operators. MightyBot improves through software discipline: change a policy, backtest it against historical transactions, promote it when it beats the current version, roll it back if it does not. Every workflow also writes to Megastore, a unified search layer, so each run makes the next one easier to investigate. Both get better over time. One does it through operator coaching, the other through versioned, testable releases.