Agentic AI products, engineered to ship.

In an agentic system the model chooses a course of action and performs it through permitted tools: reading from internal systems, calling services and completing tasks of several steps. Because the model acts on systems of record, the engineering concentrates on controlling each action it takes as much as on the quality of its output. The architecture review specifies those controls and the evaluation set tests them throughout the build.

We work with large enterprises and mid-sized organizations that need AI systems to operate reliably in production, including those in regulated industries. Engagements are typically sponsored by executives accountable for technology, operations or risk. They suit organizations with established infrastructure and standing obligations to customers and regulators.

The systems the product must integrate with, and the size of the evaluation set the use case needs, govern the timeline. The architecture review establishes both, and generally takes one to two weeks. A first system then reaches production in about twelve weeks. Where the integration surface is large or the evaluation set must be extensive, the review says so and adjusts the timeline before the build is priced.

Architecture reviews and builds are fixed-price engagements, and the review confirms the build price at its close. Advisory and operations that continue beyond the build run on a retainer. We share indicative ranges for each type of engagement in the first conversation, so leadership can assess fit before committing to a review.

The client owns them from the first week of the engagement. Accounts with model providers and cloud vendors are opened in the client's name, and the code, data and infrastructure of the system sit within them. Because the client holds every account, continued involvement from ByteForge is at the client's discretion.

ByteForge is model-agnostic. We work with hosted frontier models, or with self-hosted open-weight models, and select between them on measured performance against the evaluation set, data-residency requirements and workload cost. Every model is placed behind an interface built for substitution, so when pricing, performance or policy shifts, the provider or version can be replaced and validated against the same evaluation set.

Regulated environments are supported by design. A compliance or security team will ask where data resides, who approved each consequential action, and which data the model saw for each decision. The architecture review answers all three in writing before any build begins. Those answers rest on controls fixed in the design, from data residency to approval gates and audit trails, and on our commitment that no client data trains a model.

An engagement begins with a technical consultation. An account of the process the system would support, the systems it would touch and any constraints on data or approvals is sufficient preparation. On that call we discuss feasibility and share indicative ranges for a review and for any build that follows. Where warranted, the next step is a short, fixed-scope architecture review whose proposal states what it will examine and deliver.