AnswersAI transformation
In short
Run it as a sequence of production systems on one shared platform. Pick the workflows where value is measurable and record a baseline, connect the systems those workflows touch, and put the first one into production in weeks. Then codify how the business decides, expand system by system on the same foundation, and train models of your own once data and evaluations build up.
Choose workflows with volume, clear rules and a number leadership already tracks, such as cycle time, cost per case or revenue captured. Record today's figures; every system is measured against them.
Connect the ERP, CRM, files and tools the first workflow touches, with each source's permissions intact, and start the model of the business those systems describe.
Build the agents and the app for one workflow, with approval gates before consequential actions and a full audit trail, and run it on real work with the people who own it.
Turn operating rules, exceptions and expert judgment into instructions, evaluations and routing, so the system gets more accurate and cheaper to run every month.
Add the next workflows on the same connections, digital twin and governance. Each new system starts from what the last one built.
For the high-volume, judgment-heavy work that defines the business, train models on the company's data and decisions, and keep the hardest work on the best frontier models.
Each workflow needs a business owner who answers for its number, the people who do the work today, engineers who build in production alongside them, and security involved from the first build. An executive sponsor owns the sequence across workflows and the budget that follows the results.
Measure outcomes against the baseline every month: hours returned, cost per case, revenue captured, errors caught. Test each system on real cases, run it repeatedly, and keep one scorecard per workflow that the business and the builders both read. Benchmark scores say little about how a model will do on your own work.
Roles change as agents take on routine work. Teams learn to supervise agents, review exceptions, approve consequential actions and improve the instructions their systems follow. Training belongs in the plan from the first workflow, alongside the build.
The common failures are strategy work that never reaches a build, pilots on separate stacks that each start from scratch, no baseline to prove value, security brought in after the pilot, and a roadmap tied to one model vendor. Each one is avoidable when the first workflow is built in production on the platform every later system will share.
Fig partners at every layer of the stack. Strategy is set in weeks. Fig's forward deployed engineers build the first apps and agents on frontier models and put them into production on CosmOS. The company's rules, knowledge and evaluations then become part of the platform, and over time Fig trains models of the company's own. Fig Academy trains the teams who run it, and every engagement ends in running software measured against the baseline.
The first workflow should reach production in weeks. The transformation is the sequence that follows: each new system built on the same platform and measured monthly against its baseline, with roles and skills changing alongside it.
With one workflow that has volume, clear rules and a number leadership already tracks, built into production on real work. Applications and agents come first; models of your own come later, once data and evaluations build up.
No. Connect systems in place and model the data the first workflow needs. The model of the business grows with each workflow, and data quality improves where the work exposes gaps.
Consultancies deliver strategy and project-by-project builds, development agencies deliver one-off custom code, and in-house teams start from scratch. Fig pairs its own platform, CosmOS, with engineers who build on it, so every system shares one foundation.
Record the baseline before the build, then measure the same figures every month after launch: time, cost, revenue and quality on real cases. Report value per workflow, so the next investment follows the results.
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