Services · Codio

Four practices. One answer.

AI, software, Shopify, fashion-tech — we go deep in each. No generalist sprawl, no junior handoffs.

01To the team shipping AI without evals:

×No eval set — no way to know if the model actually works.
×No drift detection — accuracy degrades and nobody notices.
×Frontier API running when a fine tuned model would cost a third.
×A notebook in production is one restart away from an incident.

How we fix it

AI & ML Systems

Accuracy you can measure.

We build AI that earns its infrastructure cost — fine tuned models, production agents, and MLOps pipelines with evals, monitoring, and drift detection.

—Fine tuned models (LoRA, full fine tune, PEFT)
—LLM agents and RAG systems with real evals
—Computer vision — object detection, OCR, document understanding
—MLOps infrastructure — Docker, CI/CD, model monitoring

PyTorchHugging FaceLangChainLangGraphvLLMTriton

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02To the founder who paid for software their team can't maintain:

×Only the vendor who built it knows how to change it.
×Zero test coverage means every deploy is a live gamble.
×Manual deployments mean Friday afternoon dread every release.
×No documentation means months before a new engineer is useful.

How we fix it

Custom Software

Built to outlast the sprint.

Applications, internal tools, and backends engineered for the long game — not the demo. Clean architecture, owned infrastructure, and code you can maintain in five years.

—Full stack web applications
—Internal tools and operations dashboards
—REST and GraphQL APIs
—Database architecture and migration

Next.jsFastAPIPostgreSQLSupabaseRedis

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03To the brand whose store can't keep up with the brand:

×A marketplace theme that reads the same as every other store in the category.
×A SaaS widget bleeding margin every month for a feature you should own.
×An Arabic store that still ships as a flipped English layout.
×A platform that fit at launch and stopped fitting at scale.

How we fix it

Commerce

Commerce that ships.

Storefronts on Shopify and Salla, headless launches on Next.js, embedded widgets — built to ship in weeks and owned by you forever.

—Storefront builds on Shopify
—Custom Salla Twilight themes (RTL-first)
—Embedded JS widgets (vanilla, no SaaS)
—Headless commerce on Next.js + Cloudflare

ShopifySalla TwilightHydrogenNext.js

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04To the buyer still measuring fit with a spreadsheet:

×32% of returns are size-related — preventable revenue walking out.
×No fit data collected — so the problem can't be measured or fixed.
×Customers can't find what they're looking for — no visual search.
×Generic size charts don't fit your brand — or your customers.

How we fix it

Fashion-Tech Suite

AI built into the retail stack.

Size recommenders that cut returns. Visual search that converts. Smart tags that connect the garment to the catalogue. Not bolt-ons — deep integrations.

—Calibration based size recommendation
—Virtual try on and fit visualisation
—Multi channel inventory intelligence
—NFC smart tag systems

Custom CV modelsFASHN.aiShopifyPythonReact

See this practice →

Not sure which practice fits? Tell us what you're building. We'll know.

Start a project ↗

Services · Codio

Four practices. One answer.

AI, software, Shopify, fashion-tech — we go deep in each. No generalist sprawl, no junior handoffs.

01To the team shipping AI without evals:

×No eval set — no way to know if the model actually works.
×No drift detection — accuracy degrades and nobody notices.
×Frontier API running when a fine tuned model would cost a third.
×A notebook in production is one restart away from an incident.

How we fix it

AI & ML Systems

Accuracy you can measure.

We build AI that earns its infrastructure cost — fine tuned models, production agents, and MLOps pipelines with evals, monitoring, and drift detection.

—Fine tuned models (LoRA, full fine tune, PEFT)
—LLM agents and RAG systems with real evals
—Computer vision — object detection, OCR, document understanding
—MLOps infrastructure — Docker, CI/CD, model monitoring

PyTorchHugging FaceLangChainLangGraphvLLMTriton

See this practice →

02To the founder who paid for software their team can't maintain:

×Only the vendor who built it knows how to change it.
×Zero test coverage means every deploy is a live gamble.
×Manual deployments mean Friday afternoon dread every release.
×No documentation means months before a new engineer is useful.

How we fix it

Custom Software

Built to outlast the sprint.

Applications, internal tools, and backends engineered for the long game — not the demo. Clean architecture, owned infrastructure, and code you can maintain in five years.

—Full stack web applications
—Internal tools and operations dashboards
—REST and GraphQL APIs
—Database architecture and migration

Next.jsFastAPIPostgreSQLSupabaseRedis

See this practice →

03To the brand whose store can't keep up with the brand:

×A marketplace theme that reads the same as every other store in the category.
×A SaaS widget bleeding margin every month for a feature you should own.
×An Arabic store that still ships as a flipped English layout.
×A platform that fit at launch and stopped fitting at scale.

How we fix it

Commerce

Commerce that ships.

Storefronts on Shopify and Salla, headless launches on Next.js, embedded widgets — built to ship in weeks and owned by you forever.

—Storefront builds on Shopify
—Custom Salla Twilight themes (RTL-first)
—Embedded JS widgets (vanilla, no SaaS)
—Headless commerce on Next.js + Cloudflare

ShopifySalla TwilightHydrogenNext.js

See this practice →

04To the buyer still measuring fit with a spreadsheet:

×32% of returns are size-related — preventable revenue walking out.
×No fit data collected — so the problem can't be measured or fixed.
×Customers can't find what they're looking for — no visual search.
×Generic size charts don't fit your brand — or your customers.

How we fix it

Fashion-Tech Suite

AI built into the retail stack.

Size recommenders that cut returns. Visual search that converts. Smart tags that connect the garment to the catalogue. Not bolt-ons — deep integrations.

—Calibration based size recommendation
—Virtual try on and fit visualisation
—Multi channel inventory intelligence
—NFC smart tag systems

Custom CV modelsFASHN.aiShopifyPythonReact

See this practice →

Not sure which practice fits? Tell us what you're building. We'll know.

Start a project ↗