Built for Teams That Ship
Codieshub creates generative AI solutions for text, voice, vision, and gaming. Build high-quality original content at scale with production-ready AI.
Scope my generative AI build→Enterprise-grade security and compliance built into every engagement.
Nearshore teams that work U.S. hours — available for standups, reviews, and real-time collaboration.
Mid-career to senior engineers, hand-selected and tested before they ever join a client team.
From first call to first commit in 1–2 weeks. No long procurement cycles.
Consistently top-rated by verified clients across Clutch, DesignRush, and The Manifest.
Clients don't just renew — they grow with us. Annual growth in renewals reflects lasting partnerships.
Generative AI has moved from experimental to mission-critical in a span of two years — but most enterprise initiatives stall at the proof-of-concept stage because the gap between a compelling demo and a production-grade, cost-controlled system is wider than marketing materials suggest. Codieshub has been building ML-backed products since 2016, long before the LLM wave, which means our engineers understand both the statistics underneath generative models and the software engineering discipline needed to ship them reliably.
Our generative AI engagements typically span the full delivery stack: prompt architecture, retrieval augmentation, model selection and cost modeling, guardrails and output validation, API gateway design, and the observability layer that tells you when a model starts hallucinating in ways your evals missed. We do not hand you a notebook and call it done.
U.S. timezone alignment matters here more than in traditional software because generative AI work is inherently iterative — a design decision made at 10 AM needs a fast feedback loop, not a 12-hour async lag. Our senior LatAm engineers work your hours, so prototyping cycles that typically stretch across two weeks compress into days.
Most organizations have a business case for generative AI but lack the internal infrastructure to deploy it safely: no evaluation harness, no latency budget analysis, no cost ceiling guardrails, and no clear ownership of model updates when OpenAI or Anthropic ships a breaking change. Proofs of concept that look great in a demo routinely degrade in production under real traffic and real user inputs.
Codieshub architects generative AI systems with production constraints as the starting point, not an afterthought. We define evaluation criteria and failure modes before writing a single prompt, select models against latency and cost targets rather than benchmark leaderboards, and build streaming API layers and fallback chains so you are never dependent on a single provider's availability.
Engagements conclude with a deployed, monitored generative AI feature — not a prototype — complete with an eval suite, a cost dashboard, and documented handoff so your team can own it forward. Teams that move from prototype to production with the right infrastructure in place commonly see meaningful reductions in manual review burden and measurable deflection of routine support requests — the scope of those gains depends on how the system is scoped and adopted.
Get a production readiness assessment and cost model within 5 business days.
The Work
Archive · 2016 → 2026
Browse all 35 cases→
Healthcare
Healthcare SaaS for mPATH Health
Percensys Core Learning
Education
Learner & Admin Workflows for Percensys
Levers Labs
Automation
AI/ML Automation Platform for Levers Labs
Paradigm Personality Labs
HR
HR SaaS for Paradigm Personality Labs
TFX Capital
Finance
Web & UX for TFX Capital
TeamBuilder
Healthcare
Healthcare SaaS for TeamBuilder
Kiwi
Logistics
AI & ML Powered Logistics for Kiwi
Eddy
Education
EdTech SaaS for Eddy
Investment List
Fintech
Fintech Web Platform for Investor Discovery
4.9 / 5
Average client rating across platforms
93%
Net Promoter Score
150%
Client retention rate
SOC 2
Type II certified
Four ways to work with us — from surgical staff augmentation to fully managed delivery. All models share the same senior-first talent bench.
Full-time engineers embedded in your team for long-running engagements.
Explore Dedicated Teams↗Add senior specialists to an existing team — vetted, onboarded, and up to speed in weeks.
Explore Staff Augmentation↗Managed fixed-scope projects with a committed timeline and deliverables.
Explore Project Delivery↗Fractional senior technical leadership for architecture, hiring, and strategy.
Explore Virtual CTO↗Why Codieshub
The shortlist we get asked about on every call — what actually separates Codieshub from a dev shop.
We design for throughput, latency ceilings, and cost per request from day one — not retrofitted once the demo starts breaking under load.
Every generative feature ships with a regression eval suite so you know immediately when a model update changes output quality or introduces new failure modes.
We build provider-agnostic abstraction layers with fallback chains across OpenAI, Anthropic, Google, and open-weight models so uptime is never held hostage to a single vendor.
Structured output schemas, semantic content filters, and PII scrubbers sit between the model and your users — not as an optional layer but as a core delivery requirement.
Token usage dashboards, latency p95 tracking, and automated budget alerts mean finance and engineering share a single source of truth on what generative AI actually costs.
Our LatAm team operates in U.S. time zones, compressing the feedback loops that make iterative AI work go fast rather than stretching experiments across 48-hour async cycles.
Reviews
Vito Robles
COO · Percensys
Percensys case study→“They took feedback seriously, refined the details, and made sure our content and workflows were presented in a way that really works for our learners and admins.”
Lisa Dunbar
CEO · Paradigm Labs
Paradigm Labs case study→“They did an excellent job balancing scientific nuance with a user-friendly experience. It's clear they care about both rigor and design.”
Oliver Dlouhy
CEO · Kiwi
Kiwi case study→“We move fast and deal with a lot of edge cases. They kept up without cutting corners, which is rare. The team stayed responsive across time zones.”
Farid Huseynov
CEO · Kapital Bank
Kapital Bank case study→“Reliability and scalability are critical for us. They approached the engagement with a strong technical foundation and a clear process.”
Michael Ou
Founder · CoolBitX
CoolBitX case study→“Security and precision are non-negotiable for us. They demonstrated solid technical judgment, were open to feedback from our engineers, and iterated quickly.”
John Bradford
CEO · PetScreening
PetScreening case study→“An external team can be just as committed and driven as our internal one. Their dedication and attention to detail have made them invaluable.”
Ryan Pamplin
CEO · Blendjet
Blendjet case study→“Managing global scale requires extreme technical precision. Codieshub re-architected our funnels to perform under massive pressure.”
Steve Gebhardt
Founder · RSVLTS
RSVLTS case study→“Our old setup crashed during every major drop until Codieshub built a beast of an engine for us. They handled our traffic spikes perfectly.”
Davis Rosser
CEO & Co-founder · Elite Amenity
Elite Amenity case study→“The digital concierge we co-built is more than tech — it's a paradigm shift in resident experience. Luxury brands can now offer faster services.”
Enterprise-grade security and compliance across every engagement.
Nearshore teams that overlap with your working hours for real-time collaboration.
Near-perfect satisfaction scores across Clutch, DesignRush, and Manifest.
Process
Our engineers are not freelancers, and we are not a marketplace. Dedicated Codieshub seniors, seated with your team.
Before kickoff
Pre-kickoff technical and strategic review.
Before a single line of code, we sit with your team to align on stack, constraints, and what success looks like. Our VP Eng, CTO, and senior leads join — not a sales engineer.
Full review of your stack, goals, and constraints before kickoff
Session led by VP Eng, CTO, and the senior leads who'll staff the work
Architecture, tooling, and team shape agreed before the first sprint
Questions
The questions we get on every intro call — answered without the marketing gloss.
For a well-scoped feature — say, a document Q&A assistant or an automated email-drafting tool — expect 6 to 10 weeks from kick-off to production deployment. That timeline covers prompt architecture, retrieval pipeline if needed, API integration, an eval harness, and a staging-to-production cutover. More complex multi-agent workflows or fine-tuned model integrations add 4 to 8 weeks depending on data readiness.
Keep exploring
