Built to integrate with your stack, govern at scale, and survive the rollout.
We build AI systems for organizations where the hard part isn’t the model — it’s the legacy systems, the governance and security review, and the many teams that have to adopt it.
Fixed scope, one accountable lead, production in 4–8 weeks, full IP assigned to you.
Fixed scopeOne accountable leadProduction in 4–8 weeks
Why does enterprise AI stall between the pilot and the rollout?
Because the pilot was the easy part. A demo on clean sample data clears every review; the same system fails the moment it has to read a thirty-year-old order database, pass a security audit, satisfy a compliance officer, and be operated by four teams who weren’t in the room when it was built.
The blocker is rarely the algorithm. It’s integration with systems that were never designed to be queried by a model, governance that nobody built in early enough, and a change-management gap nobody owned.
Enterprise AI development is the discipline of closing exactly those three gaps — which is what separates a system that ships company-wide from a proof-of-concept that quietly expires.
Where enterprise AI development actually lands — and what each deployment changes
At enterprise scale, AI earns its keep inside specific, high-volume functions — each constrained by the same realities of legacy data, governance, and multi-team adoption. For each: what it does, the outcome it improves, and how it plays out.
01
Operations & process automation
Automates high-volume back-office work — claims handling, order processing, document review, reconciliation — by reading your existing systems and acting through them under permissioned controls. Benefit — lower cost-per-transaction and shorter cycle time, without ripping out the systems of record.
For example, an operations team routes a backlog of exception cases through an AI layer that reads the same ERP the staff use and flags only the genuine edge cases for a human — so throughput rises and the audit trail stays intact.
02
Risk, fraud & compliance decisioning
Scores transactions, flags anomalies, and surfaces compliance exceptions in real time, with every decision logged and explainable for an auditor. Benefit — faster, more consistent decisions with a defensible record.
For example, a flagged transaction is held and routed for review in seconds with the features that triggered it attached — so the analyst decides faster and the regulator sees a traceable rationale.
03
Knowledge & decision support across teams
Lets employees across departments query policies, contracts, technical manuals, and internal data in plain language, grounded in the documents you actually hold. Benefit — faster, more consistent decisions and fewer escalations, enterprise-wide.
For example, a field manager and a finance analyst each get a sourced answer in seconds against their own approved sources, instead of routing it up the chain.
04
Customer & member service at scale
Resolves high-volume service requests across channels, wired to your CRM and order systems, escalating to a person when confidence drops. Benefit — lower contact cost and faster resolution, with consistent answers across a large customer base.
For example, a routine account request resolves against live data instead of queuing for an agent — first-response time collapses and agents keep the genuinely complex cases.
05
Engineering & IT modernization
Applies AI to the software estate itself — accelerating code understanding, migration, test coverage, and maintenance on the legacy systems that AI initiatives keep tripping over. Benefit — the integration backlog shrinks and modernization moves faster. This is the same discipline behind our application modernization work.
For example, a team facing an opaque legacy service uses AI-assisted analysis to map its behavior and generate regression tests before touching it — so the change ships with confidence instead of fear.
06
Forecasting & planning
Improves demand, inventory, capacity, and workforce forecasts by learning from your operational history. Benefit — less waste and fewer stockouts or shortfalls from sharper forecasts.
For example, a multi-site operator shifts inventory ahead of a demand swing the model caught in the history — avoiding both the markdown and the empty shelf.
As of June 2026 · Revisit quarterly
What enterprise AI does at scale — the measured impact
Independent industry findings on enterprise AI, cited as third-party evidence — not Silicon Prime’s own client results.
6%
are AI high performers with 5%+ EBIT impact, while nearly two-thirds have not begun scaling AI across the enterprise — the gap is integration and governance, not the algorithm.
more likely to face AI implementation delays for organizations with fragmented or legacy systems, while up to 80% of IT budgets go to maintaining outdated systems — legacy is the integration tax.
At enterprise scale the model is rarely the constraint — integration, governance, and adoption are. We engineer for all three from the first week.
What enterprise AI development covers
This is the scope that separates a system the enterprise actually adopts from a pilot that never leaves one team. It is deliberately broader than general AI development services — the difference is everything that scale, integration, and governance demand.
01
Use-case prioritization and AI readiness
We rank candidate use cases by value and feasibility and assess whether your data, systems, and teams are actually ready — run as our AI readiness assessment, with the honest “not this one yet” call included.
02
Legacy and data integration
We connect AI to your real systems of record — ERPs, CRMs, data warehouses, mainframe-era databases — through permissioned, governed interfaces, so the system reads and acts on production data instead of a clean demo extract. This is the work most pilots skip and most rollouts die on.
03
Governance, risk, and audit
We build the governance in, not on: policy on what the system may and may not do, decision logging and explainability for auditors, model and data lineage, and human-in-the-loop gates where stakes are high — designed around your existing risk and compliance functions.
04
Security and compliance engineering
The system runs inside your own cloud tenant under your access controls; integrations use scoped, permissioned calls; data paths are documented for review; and the build is shaped to the regimes you operate under (HIPAA, SOC 2, financial-services controls).
05
Evaluation and quality gates
Before anything reaches users, it’s measured against a task-specific evaluation suite built from your real cases — accuracy, the failure modes that must never ship, and the thresholds that decide go/no-go — then monitored in production for drift and cost.
06
Multi-team rollout and change management
We ship behind a staged rollout — pilot team, then expansion — instrument it centrally, and train each team to operate the system, read its outputs, and maintain the evals. Adoption is engineered, not assumed.
What you get when you hire us — all assigned to you
A working AI system in your own cloud tenant
The integration and access-control layer into your systems of record
The same delivery model behind all our AI development work, tuned for enterprise scale — one accountable lead, fixed scope, no handoffs to account managers.
Step 01
Discover & prioritize
Rank use cases by value and feasibility, map the systems and data the AI must integrate with, and agree the success metrics and governance requirements we’ll be judged on.
Output: a prioritized plan & a readiness verdict
Step 02
Design
Build the evaluation suite from your real cases, design the integration and access-control architecture, and define the governance model — logging, escalation, lineage — with your risk and security functions.
Output: an architecture & a golden test set
Step 03
Build & integrate
Develop the system in your own cloud tenant, wired to your systems of record through governed interfaces, with guardrails, audit trails, and human-in-the-loop gates in place.
Output: a working system past the integration that kills most pilots
Step 04
Roll out & enable
Pilot with one team, measure against the kickoff metrics, then expand team by team on a shared monitoring spine, training each team to operate it.
Output: a system adopted across teams & the people who own it
Most engagements reach steady state in 4–8 weeks, full work-for-hire IP assigned at kickoff, payment structured around the ROI we agree up front.
The discipline behind AI you put into production across an enterprise
An enterprise AI system is only as trustworthy as the engineering discipline underneath it — and that discipline is what we’re known for, not slideware.
Restaurants · 200+ locations · 4 years
BJ’s Restaurants
For four years we have held a 200+ location enterprise with software-critical operations at a release cadence that moved from every two weeks to twice a week while sustaining zero critical defects — without replacing their team or their stack.
That is the enterprise-scale problem in microcosm: changing how a large organization’s software flows through governance and quality gates, integrating with what already exists, and earning the trust to ship faster because the controls are tighter, not despite them. Adjacent example: software delivery discipline, cited for the governance and quality-gate rigor an enterprise AI system demands.
It’s the same discipline — evals before launch, staged rollout, monitoring after, governance built in — that we bring to an AI system many teams will depend on. Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, offices in Los Angeles and Palo Alto, run by founder Kelvin Tran — 20+ years of production engineering, multimillion-dollar systems for one of the world’s largest automobile manufacturers, personally accountable for every engagement. Where the honest answer is “your enterprise isn’t ready for this use case yet,” we say so — which a vendor paid to ship software won’t.
Why build enterprise AI with us
01
Responsible AI is the founding charter, not a compliance afterthought. At enterprise scale, governance — what the system may do, when it must escalate, how it’s audited — is the product. We build it in from week one, which is exactly the gap Gartner attributes most failed AI value to.
02
We engineer for integration, not demos. The hard, unglamorous work — wiring AI into legacy systems of record under real access controls — is where we focus, because it’s where enterprise rollouts actually fail.
03
Engine-agnostic. We benchmark OpenAI, Anthropic Claude, and Google Gemini on your real tasks and route to whichever wins. No vendor partnership steers the recommendation.
04
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it through rollout.
05
Built to transfer. Models, prompts, evals, integrations, governance artifacts, and code are assigned to you; your teams are trained to run and extend the system when we step back.
Where enterprise AI development lands first
Healthcare
Clinical, administrative, and patient-engagement systems inside HIPAA-compliant architectures, every decision grounded, logged, and auditable. Healthcare software →
Fintech & financial services
Fraud, risk, and servicing systems where every decision carries an audit trail and a defensible, sourced rationale for regulators. Fintech software →
Multi-site operations & retail
Operations, forecasting, and service systems that integrate across many locations and the systems of record behind them — the kind of large, software-critical operation we’ve supported for years.
Questions enterprise buyers ask before building
What teams want to know before they let AI act inside their systems of record.
01How is enterprise AI development different from your general AI development services?+
Scale changes the work. General AI development services cover the build itself. Enterprise AI development adds everything that scale demands: integration with legacy systems of record, governance and audit built for compliance review, security engineering inside your own tenant, and a staged multi-team rollout with change management. The model is often the smallest part — McKinsey found nearly two-thirds of organizations stall before scaling AI across the enterprise, and that gap is integration, governance, and adoption, not algorithms.
02How do you integrate AI with our legacy systems?+
Through permissioned, governed interfaces to your actual systems of record — ERPs, CRMs, data warehouses, older databases — so the system reads and acts on production data, not a sample extract. Legacy integration is where most pilots die: IDC found organizations with fragmented or legacy systems are 30% more likely to hit AI implementation delays. We treat that integration as the core of the engagement, not an afterthought, and where the legacy estate itself is the blocker we bring our application modernization discipline to it.
03How do you handle AI governance and compliance at enterprise scale?+
We build governance in from the first week: policy on what the system may and may not do, decision logging and explainability for auditors, model and data lineage, and human-in-the-loop gates where stakes are high — all designed around your existing risk and compliance functions. Gartner attributes most unrealized enterprise AI value to incohesive governance; designing it in early is the difference between a system that passes audit and one that gets switched off after an incident.
04How do you handle data security?+
The system runs in your own cloud tenant under your access controls; integrations use scoped, permissioned calls; and every engagement starts with an NDA and a security review. Business API traffic to the major model providers isn’t used to train their models by default, and we document every data path so your security team verifies rather than trusts.
05Who owns the system when you’re done?+
You do — completely. Models, prompts, evaluation suites, integrations, governance artifacts, and code transfer under full work-for-hire IP assignment signed at kickoff, and your teams are trained to operate and extend the system. Keep us on a reduced retainer for support, or take the keys — the engagement is built around the handover.
06How do you roll out across multiple teams without it failing?+
A staged rollout, not a big bang. We pilot with one team, measure against the metrics set at kickoff, then expand team by team on a shared monitoring spine, training each team to operate the system and maintain its evals. Adoption is engineered — the change-management gap is exactly what leaves enterprise AI stuck in one department.
07What does it cost and how long does it take?+
Most enterprise AI engagements reach steady state in 4–8 weeks under a fixed-scope model with one accountable lead and payment structured around agreed ROI. Build cost depends on scope and integration complexity — our AI development cost guide gives real ranges — and the run cost (model usage plus infrastructure) is modeled before we build, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Ready to get enterprise AI past the pilot?
Bring the use case and the systems it has to live inside — we’ll tell you honestly whether your enterprise is ready, what integration and governance it really takes, and what it costs to build and run.