AI Product Development
AI Product Development Services That Launch in Sprints
You have an AI concept. Your team needs it built, tested, and generating revenue, not explored in a workshop. Redefine plans, develops, and ships end-to-end AI products for enterprise and growth-stage companies, from the first prototype to scaled production systems.
0+AI products shipped
0+
Enterprise clients
0 days
First deliverable
The reality of AI product development
Most AI builds stall before they ship.
The gap between an AI prototype and a live product that generates revenue is where most teams lose months. These are the patterns that slow AI product development down.
The old way
Months in discovery, nothing to show
Workshops, decks, and frameworks that delay the first working model by a quarter.
Model trained, product never integrated
Machine learning teams hand off a model that never connects to your user-facing application.
Siloed vendors, no single owner
Data scientists, frontend devs, and DevOps all on separate contracts. No one owns the full product.
Proof of concept that never scales
Lab accuracy of 94% collapses to 61% on real production data. No path to fix it.
No monitoring after launch
Model drift goes undetected for months. By the time someone notices, trust is gone.
The Redefine way
Sprint 1 deliverable within 7 days
A working prototype is in your hands one week after sign-off. Not a slide. Not a spec.
Full-stack AI product, one team
Machine learning engineers, backend developers, UI designers, and DevOps under a single engagement.
Production-grade from week one
Architecture decisions account for production scale. No rewrite later.
Data-tested before user-facing
Real production data validation before you put the model in front of your customers.
Observability built into the product
Drift detection, performance monitoring, and retraining triggers included at launch.
Pain · developer at fragmented systems
How every AI product build works
From Discovery to Deployed in Six Tracked Phases
Click any phase to see what happens, what you receive, and who is responsible. Every phase has a deliverable. Nothing is invisible.
1
Discovery and Opportunity Mapping
2 to 4 days
2
Architecture and Roadmap
3 to 5 days
3
Sprint-Based Development
Ongoing 2-week sprints
4
AI Model Training and Validation
Parallel to development
5
Integration Testing and QA
Final sprint + 1 week
6
Launch, Monitor and Optimize
Ongoing post-launch
Phase 01: Discovery
We map your AI opportunity before writing a line of code.
Stakeholder interviews, data audit, feasibility review, and competitive positioning. You leave with a clear problem statement, a viable AI approach, and a scope that fits your timeline.
AI Opportunity ScoreHigh
Viability
Data readiness82%
Technical feasibility91%
Revenue potentialHigh
Phase 02: Architecture
System design decisions that survive production load.
Tech stack selection, data pipeline architecture, model hosting strategy, API design, and a sprint-by-sprint roadmap. Every decision is documented with the trade-offs that shaped it.
Model layerFine-tuned LLM
Data pipelineReal-time + batch
API surfaceREST + WebSocket
InfrastructureAWS EKS
Phase 03: Development
Two-week sprints. A demo at the end of every one.
Full-stack development with sprint demos, code reviews, and documented handoff. Your team reviews working software every 14 days, not a status update.
To Do
Model API
endpoint
Rate limiting
In Progress
UI integration
Auth layer
Done
Data pipeline
Schema design
Phase 04: AI Training
Model performance validated on your production data.
Training, validation, and fine-tuning on your actual dataset. Accuracy metrics reported against your domain-specific benchmarks, not generic leaderboards. No surprises when you go live.
Training
accuracy96.4%
Production
validation accuracy93.1%
Inference
latency (p95)142ms
False
positive rate1.8%
Model approved for integration: production ready
Phase 05: QA and Integration
End-to-end testing before your users see anything.
Load testing, security audit, adversarial input testing, and user acceptance testing. A pass/fail report with every finding documented and resolved before launch.
Load test:
10k concurrent usersPass
OWASP
security scanPass
Adversarial
input testingPass
User
acceptance testing sign-offPending
Data
privacy compliancePass
Phase 06: Launch and Optimize
Your AI product is live. Drift detection starts immediately.
Blue-green deployment, real-time monitoring, drift alerts, and a retraining schedule built into the product from day one. Launch is not the end of the engagement.
Model healthNominal
Requests
(last 24 hours)47,821
Average
response time138ms
Model
accuracy (live)93.4%
Drift alertNone detected
What you receive
Enterprise-grade AI product capabilities, built and shipped by one team.
Every capability below is included or available as part of a scoped engagement. Nothing is farmed out. Nothing is invisible.
Machine Learning Model Development
Custom model training, fine-tuning, and validation on your production dataset. Accuracy reported against domain-specific benchmarks, not generic leaderboards.
Full-Stack Product Engineering
Application layer, application programming interface design, and user interface built alongside the model. Sprint demos every two weeks. Full code handoff at project close.
Data Pipeline Architecture
Real-time and batch ingestion, feature engineering, and storage strategy designed for production from the start. Not retrofitted from a prototype.
Production Deployment and Monitoring
Blue-green deployment, drift detection, alerting, and a retraining schedule built in from day one. Launch is not the end of the engagement.
Enterprise System Integration
Connects to Dynamics 365, Salesforce, custom databases, and existing frontend architectures. Application programming interface design is part of the architecture phase, not an afterthought.
Enterprise scale
Get A Scoped ProposalBuilt for teams that need production AI, not proofs of concept.
Security audit, load testing at scale, compliance review, and full intellectual property transfer. Your team owns everything at handoff.
Results in production
What shipped AI products deliver for the teams that commission them.
Case study
Demand forecasting model reduced overstock costs by 34 percent in the first quarter post-launch.
A mid-market retail operator needed to predict replenishment cycles across 2,400 stock-keeping units without adding headcount to the buying team. Redefine built a custom forecasting model, integrated it with their existing warehouse management system, and deployed it in 11 weeks. The model runs continuously in production with drift alerts wired to the operations manager's dashboard.
James Whitfield, Head of Operations, Fenbrook Retail Group
47+
AI
products built and shipped in production
11wk
Average time from sign-off to live deployment
93%
Average production validation accuracy across all shipped models
100%
Full
code and intellectual property ownership transferred to client
Why Redefine
What the typical AI development engagement looks like, and what Redefine does differently.
Typical AI Development Vendor
- Separate machine learning, development, and DevOps vendors with handoff gaps between them
- 6 to 12 weeks before anything is visible to your team
- Prototype architecture not designed for production load
- Lab accuracy reported at handoff, not production accuracy
- No monitoring or retraining plan after launch
- Time-and-materials billing where scope expands after kick-off
- Intellectual property ownership unclear or retained by vendor in contract
Redefine AI Product Development
- One integrated team: machine learning, engineering, and DevOps under a single engagement contract
- Sprint 1 delivers a working prototype within 7 days of sign-off
- Architecture designed for production load from the first phase, not retrofitted
- Accuracy validated on your production data, benchmarked against your domain, not generic leaderboards
- Drift detection, alerting, and retraining schedule built into the product before launch
- Scoped and priced before work starts, with a line-by-line breakdown by phase
- Full source code, trained models, and intellectual property transferred to you at project completion
Common questions
Answers before you commit to a call.
A focused minimum viable product with one AI capability takes 8 to 12 weeks from sign-off to production deployment. That includes discovery, architecture, development, model training, quality assurance, and launch. Sprint 1 delivers a working prototype within 7 days. Larger enterprise platforms with multiple AI features run 16 to 24 weeks. Every project is scoped before work starts so the timeline is agreed, not estimated after the fact.
Having your own labeled dataset speeds up model development, but it is not required to start. Discovery and architecture phases happen regardless. If your data is sparse, we scope a data collection and labeling strategy as part of the engagement. For certain product types, pre-trained foundation models reduce the data requirement significantly. The feasibility report in Phase 1 tells you exactly what data you need and how long it takes to get there.
3 to 4 hours per week from your side. That is one sprint review every two weeks, async feedback on deliverables via Loom or Slack, and a final quality assurance sign-off before launch. We handle architecture decisions, development, model training, testing, and deployment. You do not need to manage daily stand-ups, write technical specifications, or coordinate between sub-contractors. Those tasks belong to us.
You own everything. All source code, trained models, data pipelines, documentation, and intellectual property transfer to you at project completion. This is written into the engagement contract before a single line of code is written. We do not retain licensing rights, usage rights, or any ongoing claim to your product. The handoff includes full code documentation and a technical runbook so your team can operate and extend the product independently.
Scoped before work starts with a line-by-line breakdown by phase. No time-and-materials billing that expands after kick-off. Discovery and architecture are priced as a standalone phase so you receive a full technical plan before committing to the full build. Pricing reflects team composition, sprint count, and model complexity. You receive the full cost before you sign anything.
Yes. Integration with existing enterprise resource planning systems, customer relationship management platforms, data warehouses, and customer-facing applications is a core part of every enterprise AI engagement. We have experience connecting AI products to systems including Dynamics 365, Salesforce, custom databases, and headless frontend architectures. Application programming interface design is handled as part of the architecture phase, and integration testing is included before launch. See our AI integration services for further detail on system connectivity.
Is this engagement right for you
Read this before you submit a brief.
Good fit for Redefine
- You have a specific AI problem, not a vague "AI strategy" request
- You want a shipped product, not a research report
- Revenue, cost reduction, or operational efficiency is the target outcome
- Your team can commit 3 to 4 hours per week for sprint reviews
- You are scaling an existing business with AI, not starting from zero revenue
- You want to own the code, models, and intellectual property outright at the end
Not a fit if...
- You are a solo operator with no existing product or customer base
- Your goal is a research report, white paper, or strategy deck rather than working software
- You have no data, no access to data, and no plan to collect any before starting
- You need a full AI product live within 30 days with no prior architecture or discovery work done
- You are pre-revenue and not yet ready to invest in a production-grade build
Not sure? Tell us your situation and we will be straight with you. Submit a brief and we will respond honestly.
Start your engagement
Get Your Scoped Proposal In 3 Days.
Describe your AI workflow or automation problem. We will review it and return a scoped proposal with a line-by-line cost breakdown within 3 business days.
form
Brief received.
We will review your workflow and send a scoped proposal within 3 business days.
Response in 48 hours
A real engineer reviews every brief
Proposal in 3 days
Line-by-line cost breakdown, no vague estimates
47+ AI products built
Shipped in production across multiple industries
Full code ownership
All intellectual property transfers to you at handoff
Ready to build
Your AI product. Built to ship. Owned by you.
Submit your brief and receive a scoped proposal within 3 business days. Scoped before work starts. Line-by-line pricing. No commitment to receive a proposal.