Forge
Built in your stackYour chosen modelsYour team owns it
Custom AI, built in your stack. Deployed to production, owned by you.
HachiAI Forge builds custom AI directly on your enterprise stack and your chosen models, for the use cases that are not in our Intelligent Digital Worker catalog. Our forward-deployed engineers embed with your team, build in your production environment, and hand the working solution to you to own. Keep running it yourself, or transition eligible workflows to a HachiAI IDW to run for you.
WORKFLOWReal operationrules · controls · exceptions
MODELSClaude · GPT · Geminior open source
STACKYour infrastructureyour data · your controls
PRODUCTION ASSETOwned by you acceptance criteria met
8–12 weeksTypical time to first production release
Model-agnosticClaude, GPT, Gemini, or open source
6Industries served, operational experience across sectors
Choose the right path for the work.
Choose HachiAI Forge when the use case is unique to your business, must be built directly in your technology stack, and you want your team to own the solution after deployment. Choose a HachiAI Intelligent Digital Worker (IDW) when the workflow fits a proven role-based worker we deploy, operate, govern, and continuously improve for you.
Not sure which fits? Start with the business problem, not the technology. Whether your need is best met by a proven IDW or a custom build through Forge, the objective is the same: moving you from AI experimentation to reliable production operations. We recommend the simplest path to production.
IDW
Deployed into your systemsGoverned and managedHachiAI runs it
Three things Forge does differently.
Purpose-built for mid-market operations. Model-agnostic by design. Accountable to agreed production outcomes.
Embedded, not consulting
Our forward-deployed engineers join your standups, work in your production environment, and answer to your operating metrics: cycle time, margin, working capital, throughput. They build to agreed production acceptance criteria, not to a retainer clock.
Model-agnostic, ecosystem-native
We build on your stack, your data, and the models you already chose: Claude, GPT, Gemini, or open source. Forge does not require you to adopt a proprietary HachiAI platform and adds no lock-in, so the AI you already invested in starts producing production results.
Profitability, not just cost reduction
Defined scope, fixed fee, and agreed production milestones. Depending on the use case, value shows up as lower operating cost, faster cycle times, higher throughput, fewer errors, or stronger revenue execution. The deliverable is a production solution measured against acceptance criteria, not a set of phases marked complete.
Who builds your Forge solution.
A senior, multidisciplinary team that pairs production AI engineering with hands-on operating experience in the workflows being automated. Not generalists.
Senior AI engineers with production experience
Engineers who have deployed AI-enabled workflows across manufacturing, retail, finance, and logistics, workflows that held up against real ERPs, real users, and real exception paths.
Business operators, not just technologists
People who know AP, procurement, inventory, and finance operations, and the controls, exceptions, and approvals around them. That operating context helps the first release work under real operating conditions, not only in a demo.
End-to-end delivery, one accountable lead
Forge covers the full lifecycle, from data and workflow design through model integration, application development, deployment, and production monitoring. One accountable lead maintains context and continuity, supported by the specialists the work requires: security, data, integration, UX, and operations. You get low context loss without key-person risk.
The bottleneck to enterprise AI is production deployment. Forge is built for the mid-market.
Many companies have completed AI pilots but still struggle to move them into reliable production. The challenge is rarely choosing a model. It is turning AI into a dependable part of day-to-day operations. Large enterprises can fund specialized internal teams and long transformation programs; mid-market companies need a faster, more practical path. HachiAI Forge provides embedded, model-agnostic production engineering, delivered through a defined scope and fixed fee, with the team staying through deployment and stabilization.
Traditional AI consultingHachiAI Forge · embedded engineering
3 to 6 months of scoping and discovery2-week discovery, then engineers building in your stack
Recommendations and slide decks as the deliverableWorking software running in production
Off-the-shelf integrations and generic recommendationsCustom integrations built in your stack
Solutions built on other companies' patternsSolutions purpose-built for your business and sector
Advisors who step back before productionEngineers who stay through deployment and stabilization
Locked into one vendor's models and platformYour choice of models, your infrastructure, your data
Time and materials for the build phaseFixed fee with defined production milestones
Success measured in “phase complete” milestonesSuccess measured against agreed acceptance criteria
Five stages, from operational discovery to production deployment.
A Forge engagement runs a clear arc: understand the work, design the AI-enabled workflow, build in your stack, deploy in production, then hand off or transition to managed operations. The five stages below are what the team does, week by week.
Weeks 1–2
Understand
You finish with a working solution you own, not a roadmap. Typical time to first production release is 8 to 12 weeks for qualified use cases, and timing depends on scope, data readiness, system access, integration complexity, security review, and client decision speed. Keep running it yourself, or transition eligible workflows to a HachiAI IDW to run for you.
The last mile of enterprise AI is production deployment. Forge brings the engineering and operating discipline required to get it there.
Five reasons Forge reaches production.
Production deployment with measurable acceptance criteria and a clear business case.
- Real production deployments across retail, manufacturing, logistics, finance, real estate, and non-profit operations: pilots that reached production.
- Deep ERP integration. SAP, Oracle NetSuite, Microsoft Dynamics, QuickBooks, Yardi, Salesforce, Workday, and Epicor, integrated into your real controls and exception paths.
- You own what we build. The code, the workflow, and the operational know-how stay with your team, not locked inside a vendor platform.
- Governed from day one. Human oversight on the edge cases, your policies enforced, and a full audit trail on every action.
- Your handoff, your call. When it is ready to run, keep operating it yourself or transition eligible workflows to a HachiAI IDW managed workforce.
Forge questions buyers ask us.
How is Forge different from an IDW?
Forge is custom AI we build on your stack and hand to your team to own and run. An IDW is a managed AI worker we build, run, monitor, and improve for you, and we stay accountable to the agreed outcomes. Same engineers, two operating models: you own Forge, we run the IDW.
Explore IDWDo we own what you build?
Yes. The code, the workflow, and the operational know-how stay with your team, not locked inside a vendor platform. Keep running it yourself, or transition eligible workflows to a HachiAI IDW to run for you.
Explore IDWHow do we pay for Forge?
Forge is fixed-fee, scoped per engagement. We agree the scope, fee, production milestones, and acceptance criteria before work begins, so you know the number before you sign. You are paying for a production solution delivered against those milestones, not for billable hours or a monthly retainer. Managed IDW operations, if you choose them later, are priced separately.
Turn what you already invested in AI into a production asset.
Book a 30-minute fit and scoping call. For qualified use cases with the required information available, we send a fixed-fee proposal within two business days. If a proven HachiAI IDW is the faster or more economical path, we will point you there instead.