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Keep It Working After Launch

We put models into production and keep them there — versioning, monitoring, rollback and retraining — so accuracy does not quietly decay while the dashboard stays green

4 years

Shipping production software through bear and bull cycles

500K+

Users onboarded through the products we've engineered

50+

Projects scaled from early MVPs to live products

/Why DESH for MLOps/

Model Accuracy Decays Quietly

Rarely in a way anybody notices at the time, which is the problem

Model, prompt, dataset and config together

Rollback That Works

Everything is versioned as one unit, so reverting is a single action rather than an archaeology exercise

Teams that version the model but not the prompt discover the gap at the worst possible moment

Reproducing what is currently running should never be an open question

Tuned to fire on real degradation

Alerts People Do Not Ignore

An alerting system that cries wolf daily is worse than none, because it trains everyone to dismiss it

We use input drift plus proxy metrics where ground truth arrives late, and direct accuracy where labels come quickly

Thresholds are set against your data rather than copied from a template

Runbooks, not a retainer

Handover Is The Goal

Your team gets documentation, runbooks and a retraining pipeline they can run

Ongoing dependency on us for routine operations is a bad outcome for you and an unreliable business for us

If your team cannot operate it without us, we built the wrong thing

/What We Build/

The Layer Everyone Cuts From Scope

Serving, versioning, monitoring and retraining, plus the cost work that keeps inference affordable

Model Serving

Inference APIs with autoscaling, batching and GPU utilisation tuned to your traffic shape.

Versioning & Rollback

Every model, prompt, dataset and config versioned together, with a revert that takes a minute.

Monitoring

Latency, cost, accuracy proxies and input drift tracked continuously, with alerts calibrated to matter.

Retraining Pipelines

Scheduled or triggered retraining with automatic evaluation gates before anything is promoted.

CI/CD For Models

Tests, evaluation gates and staged rollout applied the way they already are to application code.

Cost Optimisation

Right sizing, quantisation, spot capacity and caching, applied where they pay and skipped where they do not.

/Where we step in/

Getting the first model out of a notebook, and keeping the tenth one maintainable

For a first production model

Notebook to serving API
Versioning from day one
Monitoring before the first incident
Deployment your team can repeat

First model,

no infrastructure,

production

For models live but unmonitored

Audit of what is deployed and tracked
Reproducibility checked honestly
Drift monitoring introduced
Rollback path established

Running,

nobody knows how well,

audit

For a growing model estate

Shared standards across models
Platform layer for new deployments
Inference cost reduced where it pays
Runbooks and full handover

Several models,

no standard,

platform

/Cases/

feyorra — dApp

aphone — cloud-phone

kaspa — De-Fi Platform

/Clients/

5.0

Avarage Grade

Froggik

"DESH Team maintained effective communication throughout the project."

Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the...

Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the project effectively, provided timely deliverables, and responded promptly to the client's needs throughout the engagement. DESH Team's cost-for-quality ratio stood out.

Viktoriia Bernatska

Co-Founder

ChainCrafters

"I liked their corporate policy and how they turned to customers and their wishes."

DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the cl...

DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the client's vision and utilized their wide range of resources to handle all of the client's needs and implement their ideas. The client also praised the team's insights.

Kolya Vovkun

CEO, Founder

Dropshipping

"I really like how they treat their clients."

DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done...

DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done well. DESH Team delivered on time and communicated effectively, providing responses within one hour. Their personable approach and improvement suggestions stood out.

Tetyana Yarchak

CEO

5.0

/FAQ/

FAQ’s

An audit: how they are deployed, what is monitored, what happens on a bad release, and whether anyone could reproduce the current model from scratch. That last question is the one that usually produces silence, and the answer sets the priorities.

Input drift plus proxy metrics where ground truth arrives late, and direct accuracy tracking where labels come quickly. Alerts are tuned against your data so they fire on real degradation rather than routine variation.

As often as the data shifts, which ranges from weekly to yearly depending on the domain. We make it a scheduled pipeline with evaluation gates, so the decision is driven by measurement rather than by a calendar habit.

Both. GPU workloads run in your cloud account or on your own hardware depending on cost at volume and data residency. We build so the choice stays reversible.

That is the intent. Handover includes runbooks, documentation and a walkthrough of the retraining pipeline. If your team cannot operate it without us, we have built the wrong thing.