AI Engineering Services Built for Production - Not Just a Pilot

Enterprise AI engineering has to account for scale, legacy system integration, security review, regulatory compliance, and long-term operational ownership - constraints a small-scale or consumer AI project doesn't face. It's the difference between building a working prototype and building a system your CISO, legal team, and IT operations group will all sign off on.

It depends on scope. A focused use case (a single AI agent or automation workflow) can move from discovery to production in 8-14 weeks. Broader AI-native transformation programs are typically phased over 6-18 months, with production value delivered incrementally rather than in one large release.

You own the models, code, and IP we build for you. We work within your security requirements, data residency constraints, and access controls, and we're happy to work under your preferred data handling and confidentiality terms.

Yes - this is one of our core capabilities. We design integration layers for ERPs, core banking systems, EHRs, and other legacy platforms, using APIs, middleware, and secure data pipelines rather than requiring you to replace existing systems.

Governance is built into our delivery process from the assessment stage, not added at the end. That includes access controls, audit trails, bias and quality testing, and documentation aligned to internal risk frameworks and relevant regulatory expectations for your industry.

Ongoing monitoring for model drift and degradation, cost observability for LLM usage, versioning and rollback capability, and retraining pipelines tied to production feedback - so the system you launch stays accurate and cost-efficient over time.

No. We can operate as a fully dedicated delivery team, work alongside your existing engineering team, or hand off a well-documented system for your team to own after launch - whichever fits your organization's current AI maturity.

Engagements are scoped around one of three models - advisory, dedicated team, or fixed-scope delivery - with pricing tied to scope, timeline, and team composition. We'll give you a clear proposal after an initial scoping conversation; there's no standard "package price" because enterprise environments differ too much for that to be honest.

We work most frequently with financial services, insurance, healthcare, manufacturing, and retail - industries where integration complexity, data sensitivity, and regulatory scrutiny are highest. Our governance-first approach is built for exactly these environments.

Most engagements start with a scoping conversation or our AI Readiness Assessment, which helps identify the highest-value starting point based on your data, architecture, and organizational readiness - before any commitment to a larger engagement.