Build AI products that work with real users, real data, real controls.
Most AI projects succeed as demos. Few succeed in production. We build the connected engineering layer — evaluation, RAG, orchestration, governance, and observability — that makes AI products reliable at scale.
AI Operating System — Connected Layers
$674M → $15.7B
projected growth of the AI in software development market between 2024 and 2033
Grand View Research, 202446%
of AI proofs-of-concept are scrapped before reaching production
S&P Global Market Intelligence, 2025Why AI products fail to reach production
Four recurring engineering failures that cause AI systems to succeed in demos and degrade or stall in production.
Prompt fragility in production
AI that passes internal testing breaks under real user variance, edge cases, and adversarial inputs. Systems built around prompt engineering alone — without evaluation frameworks — degrade unpredictably in production.
RAG pipelines returning wrong context
Retrieval systems that sound confident but pull irrelevant or outdated documents are worse than no retrieval at all. Poor chunking, embedding drift, and missing freshness controls cause silent accuracy failures.
No model versioning or rollback
Teams unable to identify which model version caused a regression — or revert safely — are flying blind. Without a model registry, experiment tracking, and staging environments, every deployment is a risk.
Shipping before observability is ready
Production AI with no latency tracking, hallucination detection, or cost monitoring accumulates technical debt that becomes a crisis. Observability must be designed in from the start, not bolted on after the first incident.
AI product engineering from architecture to operations
Four core disciplines that take AI from promising demo to reliable production system.
Evaluation-First AI Engineering
- Test sets and golden datasets built before any prompts are written
- Automated regression pipelines that catch failures before production
- Hallucination detection and prompt fragility testing baked in
- Every capability is measured against real user variance
- Evaluation framework that makes AI accountable, not just functional
Production-Grade RAG Systems
- Chunking, embedding, and indexing pipelines built for accuracy at scale
- Hybrid search combining semantic and keyword retrieval
- Freshness controls so your RAG system stays current
- Citation tracking — agents show their sources, not just their answers
- Retrieval evaluation that catches wrong context before it reaches users
Model Governance and Versioning
- Model registries with experiment tracking and dataset versioning
- Promotion workflows that make every model change auditable
- Safe rollback controls when regressions are detected
- Designed for regulated environments with full audit trails
- Staging environments that separate testing from production risk
Observability and Cost Controls
- Latency monitoring and token cost tracking in production
- Hallucination detection alerts before users report issues
- Drift alerting that catches degradation early
- Cost controls that prevent runaway inference spend
- Telemetry that makes AI behaviour visible and improvable
The production AI opportunity
faster task completion for developers using generative AI tools
McKinsey—Economic Potential of Gen AIrevenue uplift from AI applied consistently across customer interactions
McKinsey—Agents for GrowthChoose how CodeCones works with you
Two engagement models designed around your product stage and engineering capacity.
Product AI Engineering
We build the complete AI product layer, end to end.
- Full accountability from architecture to deployment
- Evaluation-first: test sets before prompts ship
- Governance and observability designed in from start
AI Team Augmentation
Specialist AI engineers embedded in your product team.
- Integrates into your sprint cadence and codebase
- Closes AI gaps without disrupting product culture
- Transfers production AI skills to your engineers
Your AI Partner, Not Just a Vendor
We operate with the partner standards enterprise buyers expect, with faster time-to-value and lower overhead than traditional consultancies.
Cloud Partner Certified Engineers
- Accredited on AWS, Azure, and Google Cloud
- Specializations in AI and cloud-native platforms
- Partner-tier technical access and roadmap previews
Cloud Partner Accreditations
Enterprise Security Controls
- ISO 27001 Information Security — audit-ready
- ISO 9001 Quality Management — structured delivery
- Documented data handling and incident response
Outcomes Driven Engineering Delivery
- Every engagement starts with the business outcome
- Delivery through launch, documentation, and handover
- Aligned to your timelines and constraints
AI Product Technology
Models, frameworks, and platforms for production AI.
We select the model, platform, framework, retrieval, evaluation, and serving technologies around your product requirements, data, security, and ownership constraints.
Technology selection depends on the product, existing environment, data, security, performance, and ownership requirements. The technologies shown represent selected CodeCones capabilities and are not an exhaustive list.
The AI product development opportunity
Enterprise AI adoption is accelerating. The competitive advantage goes to teams that can build AI systems that work in production — not just in workshops.
Do you need a product delivered or an engineering team strengthened?
We work with product companies, enterprises, and growth-stage businesses that need software engineering done properly. Tell us what you are building.
Outcomes-Driven Engineering — from discovery to deployment and beyond.
AI Product Development Insights
Get in Touch
Ready to Build an AI Product That Works in Production?
Tell us what you're building or what's blocking you. Our team responds within one business day.
- Dedicated project manager from day one
- Fixed-scope or continuous engagement options
- Full IP ownership — all deliverables are yours
- Response within one business day