Enterprise AI solutions built to survive procurement, security review and year three
Our enterprise AI solutions address governance, shared infrastructure and operational ownership alongside model development. We plan for procurement, security review and the teams responsible for maintaining the system.
Enterprise AI solutions are AI programmes designed for large organizations, addressing not only the technology but the governance, security review, procurement, change management and long-term ownership required for AI to operate at scale across multiple business units.
The enterprise AI programme that died in security review
Enterprise AI initiatives rarely fail on model quality. They fail because data flows were designed before anyone asked the security team, procurement took nine months, or the business unit that championed it reorganized and nobody inherited ownership.
The result is a portfolio of pilots that demonstrated value and never reached production, and an organization that concludes AI does not work at their scale.
Design for the organization, not just the problem
We involve security, compliance and procurement in week one rather than presenting to them at the end. Data flow diagrams, retention rules and vendor arrangements are agreed before the build, not reviewed after it.
And we build shared infrastructure, integration, governance, evaluation, monitoring, so each business unit builds on a foundation rather than solving the same problems in isolation.
Enterprise AI solutions: scope and deliverables
The platform layer comes first: shared integration, model access with governance, evaluation infrastructure and monitoring. Building this once means the fifth AI project costs a fraction of the first.
Governance sized to your actual regulatory exposure: acceptable use, model approval, human review thresholds, incident response and audit. Proportionate rather than copied from a framework document.
Then delivery of the initiatives themselves, sequenced so early wins fund and de-risk later ones.
And ownership: naming who operates each system, how changes are approved, and what happens when the champion moves on, which is the failure mode nobody plans for.
- Shared platform: integration, model governance, evaluation, monitoring
- Security and compliance engagement from week one rather than at review
- AI governance sized to actual regulatory exposure
- Sequenced delivery with early wins funding later initiatives
- Named ownership and operating model for long-term survival
- Change management and enablement across affected business units
Organizations this suits
Enterprises with several business units pursuing AI independently, where the duplication and inconsistency is becoming a governance problem rather than just an inefficiency.
And regulated organizations where the security and compliance path is the binding constraint rather than the technology.
- Enterprises with multiple business units running independent AI initiatives
- Regulated organizations where security review determines feasibility
- Companies with AI pilots that consistently fail to reach production
- Organizations needing AI governance before scaling adoption
- Enterprises whose procurement process AI vendors keep failing
- Groups needing shared infrastructure rather than per-unit duplication
Benefits of enterprise AI solutions
Security engaged early
Data flows and retention agreed in week one, so review approves rather than blocks a completed build.
Shared foundation
Platform infrastructure built once, so the fifth initiative costs a fraction of the first.
Governance that is proportionate
Sized to your actual regulatory exposure rather than copied from an enterprise framework nobody reads.
Ownership that survives
Named operating responsibility, so a reorganization does not orphan a production system.
Procurement navigated
We work within enterprise procurement rather than being surprised by it, including security questionnaires and insurance.
Sequencing that funds itself
Early initiatives chosen for fast, visible payback so the programme keeps its budget.
Business challenges this solves
Pilots that never reach production
Value demonstrated, deployment blocked. Security and compliance engaged from week one.
Every unit building the same thing
Duplicated integration and governance work. A shared platform removes the repetition.
Governance blocking everything
Oversized frameworks preventing adoption. Proportionate governance enables rather than blocks.
Systems orphaned by reorganization
No named owner after the champion moves. Operating model assigns responsibility explicitly.
Vendors failing procurement
Suppliers unable to satisfy enterprise requirements. We work within them from the start.
Programme losing budget in year two
No visible early wins. Sequencing prioritizes fast, demonstrable payback.
Features and deliverables
Everything below is in scope on a standard engagement. Nothing here is an upsell discovered halfway through the build.
Programme design
Initiative sequencing, dependency mapping, budget phasing and success criteria agreed with leadership before delivery.
Shared AI platform
Central integration, governed model access, evaluation infrastructure and monitoring serving all business units.
Security and compliance workstream
Data flow documentation, retention design, vendor assessment and security review run in parallel with build.
Governance framework
Acceptable use, approval process, human review thresholds, incident response and audit, sized to your exposure.
Operating model
Named ownership per system, change approval process, and support arrangements that survive reorganization.
Delivery of initiatives
The actual AI systems built on the shared platform, sequenced for early demonstrable value.
Change management
Communication, training and adoption support across affected business units.
Enablement and handover
Internal capability built so the organization can run and extend the programme independently.
Technologies we use for enterprise AI solutions
We are not tied to one vendor. Model and infrastructure choices are made on accuracy, cost per task, latency, and where your data is allowed to live.
Our AI development process
The same five stages on every engagement, so you always know what happens next and what you get at the end of it.
Discovery
We interview the people doing the work, map the workflow end to end, and audit the systems and data behind it.
AI Strategy
Every opportunity gets scored on cost to build, time to value, and annual savings, then ranked.
Pilot Build
We ship the top-ranked automation as a fixed-scope pilot so you see real output before committing further budget.
Implementation
Integration with your live systems, staff training, human-in-the-loop review gates, and a documented rollback path.
Optimization
Monthly accuracy reviews, prompt and retrieval tuning, and a written report on hours and dollars saved.
How long it takes
A typical first engagement, week by week. Complex integrations and regulated environments extend this, and we say so during discovery rather than after.
Programme design
Portfolio review, sequencing, security and compliance engagement, and governance drafting begun.
Platform foundation
Shared integration, model governance, evaluation and monitoring infrastructure built.
First initiatives
The two highest-value initiatives delivered on the platform, overlapping with foundation completion.
Governance and operating model
Framework finalized and approved, ownership assigned, support arrangements established.
Subsequent initiatives
Further initiatives delivered at reducing cost as the shared platform absorbs common work.
Industries we deliver enterprise AI solutions for
Financial Services
Document extraction, reconciliation, KYC support, and audit-ready reporting with full traceability.
Healthcare
Intake, prior authorization, clinical documentation, and revenue-cycle workflows built to respect HIPAA boundaries.
Insurance
First-notice-of-loss intake, claims triage, policy Q&A, and fraud signal detection.
Manufacturing
Quality inspection, maintenance prediction, supplier communication, and production scheduling.
Logistics & Supply Chain
Document processing, carrier communication, exception handling, and inventory rebalancing.
Professional Services
Proposal drafting, timesheet capture, research synthesis, and client reporting at scale.
Education
Enrollment support, content generation, tutoring assistants, and administrative automation.
Retail & E-commerce
Product data enrichment, demand forecasting, support deflection, and personalized merchandising.
Real-world use cases
Multi-business-unit AI programme
Coordinated delivery across units on shared infrastructure rather than duplicated independent efforts.
Regulated AI deployment
AI reaching production in financial services or healthcare with compliance satisfied by design.
Pilot portfolio rescue
Stalled pilots diagnosed and the genuinely valuable ones taken to production.
Shared AI platform build
Central infrastructure and governance enabling business units to build safely and quickly.
Post-merger AI consolidation
Overlapping AI investments across merged organizations rationalized onto one platform.
Internal capability building
Enablement so the organization runs and extends its AI programme without external dependency.
Why choose DevSolutionsAI for enterprise AI solutions
Business case before build
Every recommendation carries an estimated cost, timeline, and annual savings figure. If the math does not work, we say so before you spend.
Vendor-neutral by design
We resell nothing and take no platform commissions. Model and infrastructure choices are made on fit, cost, and your data-residency rules.
Fixed-scope pilots
The first engagement is a defined deliverable at a defined price, not an open-ended retainer that quietly grows each quarter.
Built for handover
You own the code, the prompts, the infrastructure, and the documentation. No lock-in to a proprietary wrapper you cannot leave.
Human-in-the-loop where it counts
Anything customer-facing, clinical, financial, or legal gets a review gate, a confidence threshold, and a logged audit trail.
Security reviewed early
Data flow diagrams, retention rules, and access boundaries are agreed in week one, not retrofitted after your security team objects.
Find out what enterprise AI solutions would cost you, before you commit to anything
Every engagement is quoted after a short discovery, so you get a fixed written price built around your actual volumes rather than a rate card that assumes someone else’s business.
The first call is thirty minutes and free. Bring one workflow. We will tell you what it is likely costing you each year, roughly what automating it would take, and whether we think it is worth doing at all.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
Illustrative project scenario
Taking a portfolio of stalled pilots to production
Challenge. An insurance group had eleven AI pilots across six business units. Nine had demonstrated value and none had reached production. Diagnosis found a consistent pattern: each had designed its data flows before consulting information security, and each was independently attempting to solve model access governance.
What we built. A shared platform with governed model access, central integration and evaluation infrastructure, designed jointly with information security from week one. A governance framework sized to their regulatory exposure was drafted with their risk function. Initiatives were re-sequenced by value and dependency, with two delivered on the platform during the build.
Outcome. Five of the eleven pilots reached production within nine months. Four were stopped on merit. Two were merged. Subsequent initiatives now reach production materially faster because security review is against an already-approved platform rather than a novel architecture each time.
Illustrative project scenario. The figures demonstrate how a project could be scoped and evaluated; they are not verified client results or an audited average.
What clients say about working with us
Enterprise AI Solutions FAQs
Why do enterprise AI programmes fail more than mid-market ones?
Because the binding constraints are organizational rather than technical. Security review, procurement, governance and ownership all take longer and matter more at enterprise scale, and AI initiatives routinely address them last. In the stalled programmes we diagnose, the technology almost always worked; the deployment path did not exist.
Should we build a shared AI platform first?
If you have more than two or three initiatives planned, yes. Each business unit independently solving model access, integration, governance and monitoring produces duplication, inconsistency and repeated security reviews. A shared platform costs more for the first initiative and substantially less for every subsequent one, and it means security reviews an approved architecture rather than a novel one each time.
How do you handle our security review process?
By engaging it in week one rather than presenting a completed design at the end. We produce data flow documentation, retention and access design, and vendor assessments as build artefacts rather than afterthoughts. Where security requires changes, making them in week two is cheap and making them in month five is not.
How much governance do we actually need?
Proportionate to your regulatory exposure, which is usually less than an enterprise framework template suggests and more than a mid-market business needs. We draft it with your risk and compliance functions rather than delivering a document for them to review. Governance that blocks all adoption is as much a failure as governance that permits everything.
What happens when our internal champion leaves?
This is the most under-planned failure mode in enterprise AI and we address it explicitly. The operating model names ownership per system, defines the change approval process, and establishes support arrangements independent of any individual. Systems with no named owner get switched off during the next reorganization regardless of how well they work.
What does an enterprise AI programme cost?
Programme design covering portfolio review, sequencing, security engagement and governance runs $45,000 to $90,000 over four to six weeks. A platform build plus two delivered initiatives typically runs $280,000 to $600,000. Ongoing partnership is monthly. The design phase is worth doing separately, because it frequently changes the shape of the entire programme.
Services that pair well with this one
Most clients combine two or three of these. We will tell you the right sequence during discovery.
Ready to scope your enterprise AI solutions project?
Book a free 30-minute consultation. Bring one workflow and leave with a realistic estimate of what it would cost to automate and what it would save.