Turn Your Data Into Intelligent Business Outcomes
Mars Innovation Technology builds, trains and deploys custom Machine Learning models and AI systems on your cloud data — from predictive analytics to RAG pipelines to AI agents — in production, not just in notebooks.
Delivers custom ML models trained on your data and deployed to a production API endpoint.
Covers predictive analytics, classification, forecasting, NLP, LLM, RAG and AI agents.
Includes MLOps infrastructure — model registry, retraining pipelines, drift monitoring.
Fixed price, fixed scope. Proof-of-concept in 2 weeks; production in 6–10 weeks.
ML Projects That Start in Notebooks Rarely Make It to Production
Most organisations have explored machine learning with a pilot or proof-of-concept. The problem is that getting from a working notebook to a reliable production system is a significant engineering challenge — one that data science teams are often not resourced or structured to solve alone. Models sit in Jupyter notebooks for months, delivering no value.
Even when a model is deployed, it frequently degrades silently as the real-world data distribution shifts from what the model was trained on. Without monitoring, retraining pipelines and governance processes, production ML becomes a liability rather than an asset.
Data science proof-of-concepts never reach production because engineering resources are unavailable.
Deployed models degrade silently as real-world data drifts from training data.
No repeatable MLOps process — each model deployment is a one-off effort.
LLM and RAG projects stall because teams lack the architecture and infrastructure skills.
AI projects fail to demonstrate ROI because they are not connected to real business processes.
Everything Included in This Launchpad
A complete, production-ready package — from architecture to deployment to ongoing support.
Custom Model Development
Classification, regression, forecasting, NLP and recommendation models trained on your data and evaluated against your KPIs.
LLM & RAG Pipelines
Retrieval-Augmented Generation systems that answer questions from your documents, databases and knowledge bases using OpenAI, Anthropic, or open-source LLMs.
AI Agents
Goal-directed AI agents that use tools, APIs and workflows to complete multi-step business tasks autonomously.
MLOps Infrastructure
Model registry, retraining pipelines, A/B testing, canary deployments and drift monitoring — so models stay accurate over time.
Production API Endpoint
Models deployed to a REST API with authentication, rate limiting, logging and monitoring — ready for your application to consume.
Data Feature Engineering
Feature pipeline that transforms your raw data into the inputs your models need, with lineage tracking and documentation.
Business Outcomes You Can Expect
6–10 wks
Time to Production ML
From scoping to a live model API endpoint your applications can consume.
20–40%
Improvement in Target KPI
Typical uplift in the business metric the model is optimising (varies by use case).
99%+
Model Uptime
Production endpoints with HA, auto-scaling and automated rollback.
0
Notebook-Only Deployments
Every model we build is packaged, tested and deployed as a versioned production artifact.
How We Deliver
A transparent, structured delivery plan with weekly milestones so you always know what is happening and what comes next.
Data & Problem Definition
- Data source access and EDA
- Feature engineering design
- Success metric definition
- Baseline model benchmark
Model Development
- Feature pipeline build
- Model training experiments
- Hyperparameter optimisation
- Model evaluation and selection
Production Packaging
- Model serialisation
- API endpoint development
- Authentication and rate limiting
- Unit and integration tests
MLOps Infrastructure
- Model registry setup
- Retraining pipeline
- Drift detection config
- A/B test framework
Integration & Monitoring
- Application integration
- Production monitoring dashboards
- Alert rules
- Runbook and handoff documentation
Choose Your Starting Point
Every tier is a fixed-scope, fixed-price engagement. Start small and scale when ready.
From $3,500
1–2 weeks
Data readiness assessment, use-case prioritisation, feasibility analysis and ROI estimate for 2–3 ML opportunities.
- Data audit
- Use-case scoring
- ROI estimate
- Feasibility report
- Recommended approach
From $12,000
3–4 weeks
Build and evaluate a working model for your top-priority use case. Delivered as a notebook + API proof of concept.
- 1 model built + evaluated
- Feature engineering
- Model card documentation
- API proof of concept
From $28,000
6–8 weeks
Production model deployment with MLOps pipeline, monitoring, documentation and integration with your application.
- Production API endpoint
- MLOps pipeline
- Model registry
- Drift monitoring
- Retraining trigger
- Integration guide + training
From $50,000
10–16 weeks
Multiple models, LLM/RAG pipeline, AI agents, and an enterprise ML platform with governance.
- 3+ models in production
- LLM/RAG pipeline
- AI agent workflows
- ML platform governance
- FinOps for AI spend
From $6,000/mo
Ongoing
Managed ML operations — model monitoring, retraining, performance reporting and on-call support.
- Model drift monitoring
- Retraining execution
- Monthly performance report
- On-call ML support
How We're Different
Compared to generic consultancies and do-it-yourself approaches.
| Feature / Criterion | Mars Innovation Technology | Generic Consultancy | DIY / In-House |
|---|---|---|---|
Notebook to production included | |||
MLOps pipeline built in | Extra cost | ||
LLM / RAG capability | Limited | Learning curve | |
Fixed price & scope | |||
Model drift monitoring | |||
Business KPI alignment | |||
Managed ML operations |
Frequently Asked Questions
What is the Machine Learning Launchpad?
It is a fixed-price engagement that takes a machine learning use case from data and scoping through to a production API endpoint — including model development, MLOps infrastructure, monitoring and integration documentation.
What types of ML models do you build?
We build supervised models (classification, regression, forecasting), recommendation systems, natural language processing models, LLM-based RAG systems, and AI agents — depending on your use case and data.
What is RAG and when should we use it?
RAG (Retrieval-Augmented Generation) is an AI architecture that combines a large language model with a retrieval system to answer questions from your company's documents, databases and knowledge bases. Use it when you need AI to answer questions from private or frequently-updated information that the base LLM was not trained on.
Which ML platforms do you use?
We use AWS SageMaker, Azure Machine Learning, and Google Vertex AI for cloud-based ML workflows. For LLM use cases we work with OpenAI, Anthropic Claude, Meta Llama and open-source models depending on your data privacy and cost requirements.
What is MLOps and why does it matter?
MLOps is the practice of applying DevOps principles to machine learning: version control for models, automated retraining pipelines, A/B testing, drift monitoring and governance. Without MLOps, models degrade silently over time as real-world data changes.
How much data do we need to get started?
Requirements vary by use case. For traditional ML models, a minimum of 1,000–10,000 labelled examples is typically needed. For RAG, you can start with as few as 50–100 documents. We assess your data during the Assess tier and tell you exactly what is feasible.
Do you build AI agents?
Yes. AI agents are goal-directed systems that use tools, APIs, databases and workflows to complete multi-step tasks autonomously — for example, a customer support agent that looks up account data, drafts a response and creates a support ticket. This is included in our Scale tier or as a Build tier add-on.
Can you help if we already have a data science team?
Yes. We frequently act as the MLOps and engineering arm alongside client data science teams, taking their models and delivering the production infrastructure they need to go live.
Related Launchpads
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