StronglyAI Assembly - Production AI Delivery in 6-12 Weeks

AI Assembly by StronglyAI is a delivery engagement that takes one prioritized business case from your roadmap and builds it into production AI in 6-12 weeks using lean, user-centered Labs practices adapted for ML and AI. Everything is captured into a persistent context layer that makes every future build faster.

Most AI initiatives stall between strategy and production. AI Assembly closes that gap. We take the top thin slice from your roadmap, stand up a balanced team alongside yours, and get it live in production fast. Then we iterate on the real system with evaluations, guardrails, and operations to keep it improving. Reaching production early turns opinion into evidence and starts returning value while we refine.

Example use case: A mid-market lender automating underwriting document review - we take the highest-value document type, build an AI agent that extracts and validates key fields, deploy it into their VPC with human-in-the-loop review, and iterate based on real accuracy metrics from production usage.

Phase 1 - Discovery and Framing: Confirm outcomes and KPIs, model the domain into the context layer, verify data readiness, agree the path to production, and build a backlog of outcome-oriented stories.

Phase 2 - Ship to Production: Deliver a working slice live in weeks not months, deployed to your AWS VPC or on premises, with CI/CD from week one and monitoring, evals, and guardrails at first release. Integrates with services such as Amazon SageMaker for model training and serving, Amazon Bedrock for foundation model access, AWS CodePipeline for continuous delivery, and Amazon CloudWatch for monitoring and observability.

Phase 3 - Iterate in Production: Weekly IPMs, standups, demos, and retros drive test-and-eval-driven priorities from real usage. Governance and human-in-the-loop processes mature over time.

As we run discovery and make decisions, findings are mapped into a persistent ontology: your entities, relationships, systems, data sources, terminology, and the decisions behind them. It grounds our agents with real organizational context and becomes a growing asset where each subsequent slice reuses prior domain models, compounding speed and reducing cost.

Every release ships with monitoring, observability, alerting, behavior evals in CI, drift and regression detection, model routing by cost and task, caching to cut repeat calls, and spend dashboards. Your team can operate it with runbooks and playbooks, or StronglyAI runs it as a managed service.

AI Assembly is built for cloud-native delivery on AWS. Engagements leverage Amazon SageMaker for model training, registry, and serving; Amazon Bedrock for managed foundation models; AWS CodePipeline and CodeBuild for CI/CD; Amazon CloudWatch for monitoring and alerting; and deployment into your AWS VPC with IAM-based access controls.

StronglyAI's founders brought Pivotal Labs practices to AI and ML. Pivotal Labs - the San Francisco consultancy that built software with companies like Twitter, Uber, Salesforce, and Google - was folded into VMware, and Strongly's founders spun out in 2019 to take its practices where the hardest problems had moved: AI and ML. The method includes balanced teams (product manager, designer, engineers, plus ML/AI engineering), daily standups, weekly iteration planning, pair programming, test-and-eval-driven development, and cloud-native delivery extended with model serving, a registry, and MLOps.

• Client requirements: At least one validated, high-value AI use case prioritized for production. Data access and readiness verified during Phase 1. A stakeholder sponsor available for weekly demos.
• Scope: One thin vertical slice per engagement - enough data, model, and application to reach production and return measurable value.
• Not included: AI Assembly is not a strategy engagement (see AI Primer for roadmap creation). It does not cover organization-wide platform builds or multi-slice parallel delivery in a single engagement.

• A production AI solution serving real value in your environment
• An evaluation and guardrail suite running in CI
• Context layer extensions for reuse by future builds and agents
• A path-to-production CI/CD pipeline
• Monitoring and operations runbooks
• An enabled team ready to extend and operate the system, or managed service by StronglyAI

• 6-12 weeks from kickoff to production
• Up to 10x faster from idea to production
• Up to 70% lower AI spend through routing and caching
• 20+ years of enterprise transformation experience across hundreds of engagements