Enterprise AI MVP development is about more than building a model that works. It means delivering an AI product that passes security review, fits your existing systems and earns sign-off from the business, IT and compliance teams.
SofStack builds enterprise AI MVPs that are ready for internal approval from the first release. You get a working product, a measurable business case and the documentation your stakeholders need to fund the next stage.
Why Enterprise AI Pilots Stall
Many enterprise AI initiatives produce an impressive demo and then stop. The causes are usually the same:
No clear business metric, so the project can't show return on investment
Security and legal review starts late and blocks the release
Integration gaps with identity, data platforms and core business systems
No evaluation framework for accuracy, bias or hallucination risk
Low user adoption because the tool sits outside existing workflows
Our enterprise MVP approach addresses each of these before the build begins.
Built for Enterprise Teams
CIOs and CTOs turning an AI strategy into a first delivered product
Chief Data and AI Officers moving use cases from backlog to production
Innovation and transformation leaders running AI programmes across business units
Business unit heads in finance, operations, HR, legal or customer service
Enterprise architects who need AI to fit existing platforms and standards
What an Enterprise AI MVP Includes
Enterprise Identity and Access
Single sign-on through Okta, Azure AD or Google Workspace, with role-based permissions that mirror your organisation.
Secure Data Connections
Connectors to your data warehouse, document stores and business applications, with data staying in your cloud environment.
AI Evaluation and Guardrails
Test sets, accuracy benchmarks, output filters and human review steps so the AI behaves predictably on real business cases.
Audit Logging and Monitoring
Logs of prompts, responses and user actions, plus dashboards for usage, cost and model quality.
Approval-Ready Documentation
Architecture diagrams, data flow documents, security notes and a model card for your review boards.
Stage-Gated Delivery
Every stage ends with a decision your leadership can make on evidence, not assumptions.
Stage | Duration | Key stakeholders | Decision gate |
Paid Discovery | 2 weeks | Business sponsor, IT, data owners | Approve use case, scope and budget |
Proof of Concept | 3–4 weeks | Data team, domain experts | Confirm accuracy and feasibility on real data |
Pilot | 6–8 weeks | Pilot users, security, operations | Validate adoption and KPI impact |
Enterprise MVP | 10–14 weeks | Business owner, IT, compliance | Approve rollout to wider teams |
Scale | Ongoing | Platform and product teams | Expand features, users and regions |
Governance and Reporting
You always know where the project stands, what it costs and what comes next.
Named delivery lead as your single point of accountability
Sprint demos every two weeks with your product owner and stakeholders
Weekly status reports covering progress, risks, decisions and budget
Steering reviews at each stage gate with sponsors and IT leadership
Shared tools: we work in your Jira, Confluence, Teams or Slack
Ready for Security and Compliance Review
NDA and master services agreement signed before any data access
Least-privilege access to your systems, reviewed at each stage
Private model options: enterprise LLM endpoints or self-hosted open models where data cannot leave your environment
Privacy controls: PII masking, data retention rules and GDPR-aware and HIPAA-aware handling
Security questionnaire support and documentation for vendor risk assessments
Full IP transfer of code, prompts, configurations and documentation
Integration With Your Enterprise Systems
System | Examples |
Identity | Okta, Azure AD / Entra ID, Google Workspace, SAML, OAuth |
CRM and support | Salesforce, HubSpot, Zendesk, ServiceNow, Freshdesk |
ERP and finance | SAP, Oracle, Microsoft Dynamics, NetSuite |
Collaboration | Microsoft 365, SharePoint, Teams, Slack, Google Drive, Confluence |
Data platforms | Snowflake, Databricks, BigQuery, Redshift, PostgreSQL |
Cloud | AWS, Microsoft Azure, Google Cloud |
Enterprise AI MVP Use Cases by Department
Finance
Invoice matching, expense review and variance explanations generated from ERP and spreadsheet data.
Legal and Compliance
Contract clause extraction, policy comparison and regulatory change summaries for review teams.
Human Resources
An HR policy assistant for employees and screening support for recruiters, with access limited by role.
Customer Operations
Ticket triage, reply drafting and knowledge-base answers inside your existing helpdesk.
IT and Engineering
Internal developer assistants, incident summaries and automated documentation from code and tickets.
How We Measure Business Impact
We agree on success metrics during discovery and track them through the pilot and MVP stages.
Time saved per task, per employee or per case
Cost per transaction before and after AI support
Accuracy and quality compared with the current manual process
Adoption: active users, repeat usage and user satisfaction
AI running cost per request, with optimisation targets
Enterprise AI MVP Pricing
Enterprise MVPs include identity integration, evaluation, monitoring and approval documentation. These ranges show typical starting points.
Engagement | Starting price | Timeline |
Paid Discovery | From $2,500 | 2 weeks |
Proof of Concept | From $5,000 | 3–4 weeks |
Pilot | From $10,000 | 6–8 weeks |
Enterprise AI MVP | From $20,000 | 10–14 weeks |
Dedicated AI Team | From $6,000/month | Monthly |
We support fixed-price milestones, monthly team contracts and purchase-order based invoicing.
Frequently Asked Questions
How is an enterprise AI MVP different from a startup MVP?
An enterprise AI MVP must work inside existing systems and pass internal review. It includes single sign-on, role-based access, audit logs, evaluation reports and security documentation that a startup MVP usually skips.
How long does enterprise AI MVP development take?
Most enterprise AI MVPs take 10 to 14 weeks after discovery. Timelines depend on integrations, data access approvals and the number of stakeholders involved.
Can the AI run without sending data to public models?
Yes. We can use enterprise LLM endpoints with no data retention, or deploy open-source models inside your own cloud so data never leaves your environment.
Will you work with our internal IT and security teams?
Yes. We involve IT and security from discovery, follow your architecture standards and provide the documentation they need for approval.
How do you evaluate AI accuracy?
We build a test set from your real cases, define accuracy targets with your domain experts and run automated evaluations on every release, including checks for hallucinations and unsafe outputs.
Can we start with a smaller engagement?
Yes. Most enterprises start with a two-week paid discovery or a proof of concept, then move to a pilot and MVP once the results are proven.
Do you sign MSAs and work through procurement?
Yes. We work with your master services agreement, NDA, statements of work and purchase-order process.
Plan Your Enterprise AI MVP
Share your use case and stakeholders. We will propose a starting engagement, a stage-gated plan and a cost estimate within one business day.