Since 2010
Building Software
The ML Ecosystem, Engineered Properly.
TensorFlow Development Services
We build and deploy production ML with TensorFlow: custom models, TFX training pipelines, TF Serving infrastructure, and TFLite builds for mobile and edge, with monitoring and full source ownership.
- 120+
- AI Engineers
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 3,900+
- Apps Published
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
Where tensorFlow is the right choice
TensorFlow remains the pragmatic choice when you need a complete production story: TFX for pipelines, TF Serving for high-throughput inference, TFLite for mobile and embedded, and TensorBoard for visibility, all first-party. For research-heavy modelling PyTorch often leads; for deployment-heavy systems TensorFlow's tooling earns its keep.
We are fluent in both and choose per project with reasons documented. Our broader modelling practice sits under machine learning development, where TensorFlow is one instrument among several.
What our tensorFlow development services cover
The lifecycle: data pipelines, training, serving, and edge.
Custom model development
Keras and low-level TF models for classification, prediction, and vision tasks, validated against your baseline before adoption.
- ✓Keras architectures
- ✓Custom layers and losses
- ✓Baseline comparisons
- ✓Reproducible training
TFX training pipelines
Production training pipelines with data validation and model analysis, so retraining is routine rather than heroic.
- ✓Data validation
- ✓Model analysis
- ✓Orchestrated retraining
- ✓Model registry
TF serving deployment
High-throughput inference services with versioning, canarying, and latency budgets at real traffic.
- ✓Versioned servables
- ✓Canary rollouts
- ✓Latency monitoring
- ✓Autoscaling configs
TFLite and edge builds
Quantised models for mobile and embedded hardware, tested on target devices for latency and accuracy trade-offs.
- ✓Quantisation and pruning
- ✓Device latency testing
- ✓Accuracy trade-off reports
- ✓OTA model updates
TensorFlow to production migration
Notebook models hardened into versioned, tested, monitored systems your team can operate.
- ✓Code refactoring
- ✓Test coverage
- ✓Pipeline packaging
- ✓Runbook documentation
Framework migration and rescue
Legacy TF1 codebases modernised, or models moved between frameworks with regression evaluation guarding behaviour.
- ✓TF1 to TF2 upgrades
- ✓Cross-framework ports
- ✓Regression test suites
- ✓Performance profiling
What every engagement includes
Every TensorFlow project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped TensorFlow roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All TensorFlow source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with tensorFlow
Systems our engineers have delivered on the stack.
On-device ML features
TFLite models powering in-app features like image capture, text scoring, and personalisation, fully offline.
High-throughput inference APIs
TF Serving deployments handling production traffic with versioned models and canary rollouts.
Automated retraining pipelines
TFX pipelines that validate data, retrain on schedule, and promote models only when evaluation improves.
Vision and forecasting models
Custom models for defect detection, demand forecasting, and scoring, benchmarked against your current process.
Modernised legacy models
TF1-era models upgraded to current stack with regression tests proving behaviour is preserved.
How a TensorFlow Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your model goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The TensorFlow architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. TensorFlow work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged model launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack around our tensorFlow builds
Default choices, stated with reasons.
Modelling
Keras as the default interface with low-level TF where performance demands it, and experiment tracking on every run.
Pipelines
TFX components for validation, training, and analysis, orchestrated on managed schedulers with lineage captured.
Serving
TF Serving for server deployments, TFLite for mobile and edge, with latency and accuracy budgets set per target.
Quality
Model analysis views, regression suites, and production monitoring, so model changes ship with evidence.
How intelligence flows through the stack
What each stage reads, and what it changes.
Data validation
Checks incoming training data against schema and drift expectations.
Reads
Training datasets, schema definitions
Changes
Validation reports and gates
Training pipeline
Trains and evaluates versioned models reproducibly.
Reads
Validated datasets, hyperparameters
Changes
Model registry entries
Serving layer
Serves models with versioning and monitoring at traffic.
Reads
Model registry, inference requests
Changes
Prediction logs and metrics
What's included, standard
The baseline on every TensorFlow engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each TensorFlow release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Model review session
A working review of your existing models with improvement and deployment recommendations, documented.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for tensorFlow work
The differences live in engineering habits, not pitch decks.
Full-lifecycle fluency
Training pipelines, serving, and edge deployment are all first-class for us, so models do not stall at the notebook stage.
Framework-neutral advice
We recommend TensorFlow when its tooling fits and say so plainly when PyTorch or simpler tools serve better.
Baseline-first modelling
Every model is validated against a measurable baseline before adoption, so complexity is earned by evidence.
120+ AI engineers
Senior ML engineers with production TensorFlow deployments across vision, forecasting, and on-device work.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with production ML among them.
TensorFlow development FAQs
$ moxace launch --nowReady to Ship TensorFlow to Production?
Talk to our TensorFlow team about a fixed-scope build with pipelines, serving, and full source-code ownership.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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