Enterprise AI Development

A focused PoC runs 4–6 weeks. A production-ready capability for a single high-value use case typically takes 10–16 weeks: 2–3 weeks discovery and AI strategy, 6–8 weeks build and integration, 2–3 weeks for security hardening and governance, then pilot and production rollout. The largest variable is system accessibility — well-documented APIs make integration fast, while legacy systems without API layers add time for connector work. We sequence delivery so you see working software early and the security review never becomes a surprise at the end.

By building to the security requirements from day one rather than retrofitting them: identity-first access through your IdP with least-privilege scopes, no standing credentials in the runtime, data residency and isolation options up to fully self-hosted deployment, and a complete audit trail of every action and data access — plus the architecture, data-flow, and control documentation your security team needs to evaluate the system. We design to be compatible with the controls behind common frameworks — GDPR, SOC 2, ISO 27001, HIPAA, and emerging AI regulation such as the EU AI Act — mapping specific obligations to concrete system controls during discovery. The goal is for the security review to confirm controls already in place, not become an investigation that sends the project back to the drawing board.

We translate your responsible-AI policy into controls enforced in the system, not guidelines the AI is trusted to follow. Data-access rules, approved use cases, and prohibited actions become runtime checks the AI cannot bypass. High-impact actions route to defined human-approval gates while low-risk ones run autonomously, so oversight scales. Every output carries its sources and reasoning context for explainability, and we track model version, prompt, and data lineage behind each result. Because enforcement lives in the orchestration layer, the rules hold no matter which model runs underneath or how a user phrases a request.

Yes — integration is the hard part of enterprise AI, and it's a core strength for us. We connect AI to ERP, CRM, data warehouses, identity providers, and line-of-business systems through whatever they expose: modern APIs, message queues, database reads, or batch files. For legacy systems without clean APIs, we build a structured integration layer so their quirks are contained rather than leaking into the AI logic. When the AI writes back into systems of record, we add validation, idempotency, and rollback paths. And the AI inherits your existing permission model rather than bypassing it — so integration doesn't open a new security hole.

It's the most common failure mode in enterprise AI: a pilot proves the idea but was never engineered to survive production. The jump requires reliability under real load, sane unit economics, monitoring for quality drift, security sign-off, and a clear operational owner — the unglamorous work pilots skip. We avoid this by treating the PoC as the first stage of a production system, not a throwaway prototype: the architecture, security model, and integration approach are production-grade from the start, so scaling is a continuation rather than a rebuild. Where a prior pilot stalled, we can often diagnose exactly which of these gaps blocked it.

Yes, and ownership is unconditional. We support deployment from your managed cloud account (AWS, Azure, GCP) to fully self-hosted, air-gapped environments where data never leaves your perimeter and the system runs on open-weight models you control — so for financial services, healthcare, or public sector with strict residency or sovereignty rules, sensitive data is processed inside your boundary and we never need direct access to production data. And all IP is yours: your data, your models, your outputs, and all code we produce. We execute NDAs before any discovery conversation, never retain client data after a project concludes, and never use your data to train models that benefit other clients. The system, and everything it learns from your data, stays yours.

Hallucinations — plausible-sounding but incorrect outputs — are a primary risk in production AI, and we address them at multiple layers. RAG grounding bases answers on your retrieved source documents with citations; structured outputs are validated against a schema before use; low-confidence outputs trigger a human-review gate; and we include hallucination-specific cases in the evaluation suite. We define an accuracy benchmark and acceptable error rate with you before launch, and monitor for quality drift in production. The goal isn't a system that's never wrong — it's a system whose error rate is measured, bounded, and caught before it reaches a decision that matters.

It depends on how strategic the capability is and how unique your data and processes are. Buy the commodity — where a mature product solves a generic problem, there's no advantage in rebuilding it. Build the differentiator — where AI touches your proprietary data, workflows, or competitive edge, custom build keeps the advantage yours rather than your vendor's. Partner for the gap — when you need to move fast or lack in-house AI depth, a delivery partner builds it with you and transfers ownership. We help you make this call honestly, including telling you when to buy rather than build, because we weigh the real total cost — integration, lock-in, data exposure, and operating burden — not just the sticker price.

ROI is provable, but only if you decide what you're measuring before you build. We agree upfront on the metric that matters — hours saved, cycle time, error rate, conversion, cost per case — and establish a baseline. The system is then instrumented to capture usage and outcome data by design, so impact is measured from real activity rather than estimated after the fact. Our ROI accounting includes the full cost — model, infrastructure, integration, and operations — so the net value holds up under finance scrutiny. Where possible we compare against a control or pre-AI baseline so improvement is credibly attributed to the AI, giving leadership evidence to fund scaling.

Most engagements start with either an AI Strategy & Discovery Sprint or a fixed-price PoC. The discovery sprint interviews stakeholders, audits your data and systems, prioritises your highest-value use cases, and produces an implementation roadmap with effort, timelines, and ROI projections — which you own outright. The PoC runs 4–6 weeks with fixed scope and budget (typically $15,000–$25,000), with measurable success criteria agreed before any work begins: it delivers a working system on your real data, integration with at least one live system, a defined governance and security-review path, and an executive-ready ROI assessment. Critically it's built on production-grade architecture, so if you proceed you're extending the PoC toward production rather than throwing it away and starting over.

Yes — production AI needs ongoing attention, and we offer managed AI and MLOps support. Our standard engagement includes a period of included support after launch: monitoring, incident response, model-performance review, and minor adjustments. Beyond that, support retainers cover model-drift monitoring, periodic re-evaluation against fresh data, cost optimization, feature additions, and integration maintenance as your underlying systems change. Because models degrade quietly rather than failing loudly, this monitoring is what keeps quality and cost in check long after the launch announcement — and it's why we define operational ownership before go-live, not after.