After working with Redefine
A working system in production, not a notebook
First deployment within 8 weeks of sign-off. Evaluation harness built before training starts.
42 AI systems shipped · 30+ enterprise teams served
Build AI systems that ship to production
Redefine engineers production-ready AI systems for enterprise and growth teams. From model selection through deployment and monitoring, you get working software with measurable outcomes, not a notebook and a handoff slide.
Systems in production
42
across enterprise teams
The engineering challenge
Most AI projects never reach the systems they were built for
CTOs and engineering leads tell us the same three things. Each one has a direct fix.
What we hear from CTOs
Prototype stuck, production nowhere
"We have a working prototype that has been almost ready for eight months. Getting it into production has been the hard part."
After working with Redefine
Real data handling with tested fallback logic
Every model tested on production-representative data. Degradation alerts built before go-live.
What we hear from VP Engineering
Test data passes, production breaks
"The model performs well in test data. It breaks in ways we did not anticipate once real production traffic hits it."
After working with Redefine
A line-by-line scoped proposal in 3 days
No commitment required to receive a proposal. Every deliverable named and priced before Sprint 1 starts.
What we hear from Operations Leads
Vague proposals, unclear deliverables
"Every vendor gave us a three-slide proposal and asked for six months upfront. We still have no idea what we are actually getting."
Built for your role
What you need depends on where you sit
Select your role to see how Redefine solves for your specific challenge.
Your role
Chief Technology Officer
You need AI that runs in production, not in a demo
Your team built a proof of concept. Leadership wants production results. The gap between prototype and a reliable system is where most projects stall.
- Production deployment with monitoring and alerting from day one
- Governance dashboard with model registry and full audit logs
- Compliance review baked into the delivery process, not added at the end
Your role
VP Engineering
You need a team that ships machine learning code, not just notebooks
You have engineers who write Python but not machine learning engineers who take models to production. The gap between a trained model and a reliable API is expensive to close alone.
- Sprint-based delivery with clear model evaluation gates each sprint
- MLOps pipeline configured before the first model is trained
- Handoff documentation and internal team training at every milestone
Your role
Operations Lead
You need automation that holds up under real volume
You have a high-volume process running on spreadsheets and manual steps. AI should handle it. Nobody has scoped what that actually looks like yet.
- Clear process map and automation spec before any code is written
- Human-in-the-loop checkpoints built into every automated flow
- Error handling and fallback logic reviewed before go-live
Your role
Head of Product
You need AI features that move your core product metrics
Every competitor is shipping AI features. You need features tied to conversion, retention, or engagement, not AI added for its own sake.
- Feature design tied to measurable key performance indicators before a single model trains
- Evaluation framework built and agreed before training starts
- Staged rollout plan with feature flags and revert controls
Deployed Models
enterprise-search-v3
847k calls/day · 94.2% accuracy · 23ms p50
Deployed
classification-llm-v2
12k calls/day · 89.1% accuracy · 41ms p50
Staging
document-extract-v1
Evaluation in progress · Sprint 7
Testing
SOC 2GDPRInternal Audit Q1
Sprint 6 of 10
On track · 34 pts/sprint
Backlog
Load testing
5 pts
In Progress
Fine-tune recall
8 pts
Review
API contract
3 pts
Done
Data prep
13 pts
Sprint velocity
29 of 34 points completed
Before
3 staff · 4 hours/day
12% error rate
After
AI-assisted · 40 min
0.3% error rate
Automated workflow
Ingest
AI
Extract
AI
Validate
Human
Route
AI
Approve
Human
Semantic Search
100% rollout+12% conversion rate
Smart Recommendations
40% rolloutTesting
Auto-categorization
10% rolloutEarly data
Predictive Reorder
Development · Q2 target
In dev
What we build
Every capability your production AI system needs
From the first model experiment to monitored production deployment. Delivered as sprint-scoped work, not open-ended retainers.
Core capability
Model Development and Fine-Tuning
Foundation model integration, domain-specific fine-tuning, retrieval-augmented generation, and custom model training when off-the-shelf models fall short. Every approach evaluated against your business metric before training starts.
Foundation model integration
Domain fine-tuning
Retrieval-augmented generation architecture
Evaluation harness design
MLOps and Production Deployment
Continuous integration and deployment pipelines for machine learning, model monitoring, drift detection, rollback automation, and alerting. Built before the first model goes live, not bolted on after.
AI Application Programming Interface Engineering
REST and GraphQL AI application programming interfaces, streaming inference endpoints, async job queues, and latency optimization. Built to your integration spec, not a default template.
Data Pipeline Engineering
Extract, transform, and load pipelines designed for AI workloads, vector database setup, embedding pipelines, and data quality validation. Your model is only as good as your data infrastructure.
AI Security and Compliance
Data privacy controls, model access management, audit trails, prompt injection defense, and compliance review. Built in, not added as an afterthought.
Part of the Redefine AI platform. Explore the full AI services suite or see AI consulting pricing.
Proof
Real systems. Real results.
Corporate Gear
B2B Ecommerce / ERPCorporate Gear serves businesses with branded merchandise, apparel, and promotional products. Their platform handles complex catalog operations, multi-system data, and high-volume order processing.
Revenue outcome
$0M+
annual revenue achieved through data-driven optimization and automation systems
Systems integrated
6+
ERP, customer relationship management, business intelligence, email, analytics, inventory
Optimization cycles
A/B
Ongoing test and learn across all platforms
The challenge
Corporate Gear needed to improve usability, accessibility, and conversion performance while competing in a crowded market. Data was fragmented across ERP, customer relationship management, and marketing tools, creating operational bottlenecks. Testing frameworks were underdeveloped and personalization was not in place.
What we built
A comprehensive data-driven optimization system implemented across the commerce platform. AI-assisted A/B testing identified high-performing layouts and messaging. ERP and analytics integrations unified operational and customer data into a single decision layer. Personalization and segmentation systems were deployed across all key conversion paths. Search engine optimization and performance improvements compounded gains across paid and organic channels.
Delivery methodology
From problem statement to production system
Every engagement follows the same six-phase structure. Each phase has defined deliverables, owner actions, and exit criteria before the next phase starts.
1
Scope
Days 1-2Problem framing, data audit, feasibility assessment
2
Design
Days 3-5Stack selection, integration design, security review
3
Build
Weeks 2-6Model development, API engineering, sprint delivery
4
Integrate
Weeks 7-8System connection, data pipelines, staging environment
5
Deploy
Days 1-3Production rollout, monitoring setup, alerting
6
Optimize
OngoingDrift detection, performance tuning, iteration
Phase 1 - Scope deliverables
Problem framing documentDelivered
Data availability assessmentDelivered
Feasibility and risk briefDelivered
Go/no-go recommendationDelivered
Your team's time: 2-hour discovery call. We write the brief.
Phase 2 - Architecture decisions
Model layerGPT-4 fine-tune + retrieval-augmented generation
Selected over custom training based on data volume and latency targets
Vector storePinecone / Weaviate
Evaluated against Qdrant and Chroma for your query patterns
Integration surfaceREST API + webhook
Async job queue for batch operations, sync for real-time queries
Phase 3 - Sprint 4 of 8
Build progress62%
Embedding pipelineDone
Retrieval API v1Done
Re-ranking layerIn progress
Eval harnessQueued
Phase 4 - Integration status
Core platform APIConnected
Vector store syncConnected
Webhook deliveryTesting
Load test (1k requests per second)Queued
Phase 5 - Production deployment
Blue/green deployment configured
Monitoring dashboards live
Latency alerts active
Model drift detection running
System live - p99 latency 47ms
Phase 6 - Optimization signals
Accuracy94.2% up
Up from 91.8% at launch. Drift threshold: 88%
p50 latency23ms down
Down from 41ms. SLO target: 50ms
Next iterationQueued Sprint 12
Re-ranking layer update based on 30-day user feedback
Why Redefine
What separates proven delivery from broad promises
Most AI vendors can show you a demo. Not all of them can show you a production system running on your data at your scale.
Capability
Redefine
Typical AI vendor
Ideal client qualification before scoping
Proof tied to delivery type, not general case studies
Production-grade MLOps from sprint one
Existing system integration as core scope item
Post-launch drift detection and monitoring
Security and compliance review in the delivery process
Fixed-sprint delivery cadence with exit criteria per phase
You own the intellectual property, code, and models at every stage
Common questions
What most CTOs ask before starting
Search and retrieval systems, document processing pipelines, recommendation engines, classification and routing systems, conversational AI with production retrieval-augmented generation, and custom machine learning models for prediction and forecasting. The scope is defined by your business problem, not a pre-packaged product. We evaluate whether a foundation model, fine-tuned model, or custom-trained model best fits your use case before any code is written.
A first production deployment of a focused AI system takes 8 to 12 weeks from signed scope. A more complex platform with multiple integrated components runs 14 to 20 weeks. The scope document defines the timeline before Sprint 1 starts. Every project uses the same six-phase structure: scope, design, build, integrate, deploy, optimize. You see the timeline before you commit.
Both, based on your use case. The large majority of production enterprise AI systems benefit from using an existing foundation model with domain-specific fine-tuning and retrieval augmentation rather than training from scratch. We run a feasibility assessment during the Scope phase that evaluates your data volume, latency requirements, accuracy targets, and total cost of operation before recommending an approach. If a foundation model is sufficient, we will not recommend custom training.
Two-week sprints with a 45-minute sprint review at the end of each. You give async feedback on deliverables via a shared workspace: no long review meetings or status calls. Your team's involvement is 3 to 4 hours per week. We manage all day-to-day decisions, standups, testing, and documentation. You review outputs, approve direction changes, and sign off on each phase's exit criteria before we move to the next.
A security review is part of the Design phase, not an add-on. We review data handling requirements, personally identifiable information exposure in training data, model access controls, prompt injection attack surfaces, and audit trail requirements before a line of production code is written. If your business requires SOC 2, GDPR, HIPAA, or CCPA alignment, those constraints shape the architecture from day one rather than being retrofitted at the end.
Who we work with
Is this the right fit?
We work well with some teams and not others. Here is an honest read.
Good fit for
- Enterprise or growth teams with a defined AI use case and real production data
- Teams that have a working prototype and need it in production, monitored, and integrated
- Organizations that want to own the code, models, and intellectual property outright
- Leaders who can give 3 to 4 hours per week and want a sprint-based, scoped engagement
Not a fit if
- You need a proof of concept or demo built with no intention to take it to production
- You have no existing data or a use case that is not yet defined enough to scope
- You need a full system built in under 4 weeks with no review cycles
- You are a solo operator or pre-revenue startup without an engineering counterpart
Not sure? Tell us your situation and we will be straight with you.
Start here
Tell us what you are building
Scoped before work starts · line-by-line pricing · no commitment to receive a proposal.
Response within 48 hours
A senior engineer reviews your brief and follows up directly
Scoped proposal in 3 days
Line-by-line deliverables, timeline, and pricing before Sprint 1
You own the intellectual property from day one
Code, models, and data stay yours: no licensing lock-in
42 AI systems shipped
Across 30+ enterprise teams, from first deployment to ongoing optimization
Form
Call within 48 hours · proposal in 3 days · Sprint 1 within 1 week of sign-off
Brief received
Brief received, we'll review your workflow and send a scoped proposal within 3 business days.
Ready to ship
AI development with no ambiguity about what ships
No commitment. No pitch. A scoped proposal in 3 days.
0
AI systems shipped to production
0+
enterprise teams served
3 days
to a scoped proposal
8 weeks
first production deployment
