Everything you might want to know about our AI, cloud, data, security, funding and staffing services — grouped by topic. Can't find your answer? Reach out and an architect will help.
AI consulting helps your business decide where AI will create real value. At Mars Innovation Technology we map your current processes and data, identify high-impact AI opportunities, recommend the right architecture (LLM, RAG, AI agents or Agentic AI) and deliver a clear, prioritized roadmap with realistic timelines and costs.
Machine Learning (ML) is software that learns patterns from your data instead of being explicitly programmed. We build custom ML models for forecasting, classification, recommendations, anomaly detection and decision support, then deploy them into your business systems with proper monitoring and evaluation.
A Large Language Model (LLM) is an AI model trained on huge amounts of text. It is the “thinker” — used for reasoning, writing, summarizing, drafting answers and powering chat experiences. We use LLMs as the reasoning core inside AI assistants, customer support tools and internal applications.
RAG is the “researcher”. It connects an LLM to your own documents, policies, PDFs, manuals, websites, databases and knowledge bases. Instead of guessing, the AI retrieves the most relevant content and uses it to produce accurate, source-cited answers from your private data.
Agentic RAG is an advanced AI system that retrieves data, reasons through multiple steps, uses tools and APIs, verifies its answers, refreshes its memory and takes action across business systems. It combines the strengths of RAG (private knowledge) with AI agents (action) for complex workflows.
An AI agent is the “doer”. It is an AI system that can complete real business tasks using tools, APIs, CRMs, databases, email, calendars, ticketing systems and workflows — not just answer questions. Examples include lead qualification, ticket triage, document processing and outbound follow-ups.
Agentic AI is the “coordinator”. It orchestrates multiple agents and tools to manage multi-step workflows across systems, departments, data sources and business processes — with clear hand-offs, approvals and audit trails so people stay in control.
AI can reduce manual work, speed up decisions, improve customer experience, qualify leads, automate routine support, find insights in your data and integrate information across systems. The right starting point depends on your business — that is what an AI readiness assessment is for.
Yes. With a RAG system we securely connect AI to your SharePoint, Google Drive, intranet, wikis, PDFs, manuals, policies and websites. Your data stays under your control and the AI answers with citations to the source documents.
Yes. We build private AI assistants that index your PDFs, policies, manuals and internal files and answer plain-language questions with citations. We respect existing permissions so users only see what they are allowed to see.
Yes. We design AI agents and Agentic AI workflows that automate support triage, sales follow-up, document processing, internal operations, reporting and other repetitive multi-step tasks — with human approval steps where appropriate.
Yes. We integrate AI with the systems you already use — CRMs, ERPs, ticketing platforms, databases, email, calendars, dashboards and custom APIs — so AI works inside your existing operations, not as another silo.
Yes. We design AI chatbots and AI search experiences for websites that answer customer questions from your real content, qualify leads, route conversations to the right team and stay on-brand. They can also hand off cleanly to a human agent.
Yes. We build private, secure knowledge assistants for internal teams (HR, IT, support, operations) using RAG over your own documents. Data is kept under your control and the assistant answers only from your approved sources.
Yes. AI can deflect routine questions, summarize tickets, draft replies, look up customer data and resolve common issues automatically — while sending complex cases to your human agents with full context.
Yes. We build AI agents that score and enrich inbound leads, draft personalized outreach, log activity to your CRM and book meetings — so your sales team spends more time on real opportunities.
Yes. We build AI tools that summarize long email threads, contracts, meetings and reports, extract key actions, and push results into your CRM, ticketing or project management tools.
Yes, when built responsibly. We focus on private data handling, access controls, prompt and output safety, human-in-the-loop approvals, logging and monitoring. We help you adopt AI in a way that fits your compliance, privacy and risk requirements.
A focused proof-of-concept is typically delivered in 2–6 weeks. A production-ready AI assistant, agent or RAG system usually takes 1–3 months depending on integrations, data sources and security requirements.
The easiest first step is a short AI readiness assessment. We meet your team, look at your processes and data, identify 2–3 high-value AI use cases and propose a clear roadmap. From there we can deliver a pilot and scale into production.
It is a fixed-price engagement that delivers a production-ready CI/CD pipeline, Kubernetes cluster, infrastructure-as-code, secrets management, and observability stack for your engineering team in 4–6 weeks.
Yes. For greenfield projects we set up the full cloud-native foundation before you write your first line of application code. For legacy apps we containerise and pipeline-enable the existing application without requiring a full rewrite.
We use GitHub Actions, GitLab CI/CD, Azure Pipelines, or AWS CodePipeline depending on your existing tooling. All pipelines follow GitOps principles with merge-based promotions and automated rollback.
We deploy and configure Amazon EKS, Azure AKS, and Google GKE. We also support lightweight distributions such as K3s for edge or cost-sensitive workloads.
We integrate HashiCorp Vault or cloud-native secrets managers (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager) and configure your pipelines to inject secrets at runtime rather than storing them in code or environment files.
We deploy Prometheus and Grafana or equivalent cloud-native tooling (AWS CloudWatch, Azure Monitor, Google Cloud Monitoring). You get pre-built dashboards for your applications and infrastructure, plus alerting rules from day one.
Each tier is fixed price and fixed scope. We confirm the exact scope during the free Assessment. Cloud infrastructure costs are billed to your account separately.
Yes. Our Application Modernization service provides a pathway from monolith to containerised services. The Cloud-Native Development Launchpad then provides the deployment infrastructure for those services.
It is a fixed-price engagement that designs, builds and deploys a production-grade data ingestion, transformation and delivery pipeline on AWS, Azure or GCP — including orchestration, data quality, lineage and documentation.
We connect relational databases (PostgreSQL, MySQL, SQL Server, Oracle), SaaS applications (Salesforce, HubSpot, Shopify, Stripe), event streams (Apache Kafka, Amazon Kinesis), flat files (S3, SFTP, GCS) and REST APIs.
Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics, and Databricks Delta Lake are all supported. We design the loading and transformation patterns appropriate for each platform.
Yes. Streaming ingestion via Apache Kafka, Amazon Kinesis, or Azure Event Hubs is available in the Build tier and fully productised in the Scale tier for near-real-time use cases.
dbt (data build tool) is the standard for SQL-based data transformation. It version-controls your transformations, generates documentation automatically, runs tests on every model, and produces column-level data lineage. It turns your data warehouse into a reliable product rather than a collection of ad hoc queries.
Every pipeline run includes automated row-count checks, schema validation, freshness assertions, and statistical anomaly detection. Failures trigger alerts and stop downstream processes from consuming bad data.
Yes. We document your current ETL jobs during the Assess tier, build equivalent or improved pipelines, and run in parallel before cutover to ensure nothing is lost.
Managed Data Pipeline covers 24/5 pipeline monitoring, incident response for failures, schema change management when upstream sources change, monthly data quality reports, and a quarterly architecture review.
It is a fixed-price engagement that automates your end-to-end build, test and deploy pipeline on AWS, Azure or GCP — including infrastructure-as-code, environment parity, monitoring and runbooks — delivered in 4–6 weeks.
We work with GitHub Actions, GitLab CI/CD, Azure Pipelines, AWS CodePipeline, Jenkins and Bitbucket Pipelines. We integrate into your existing platform rather than requiring a migration.
Web applications, APIs, microservices, background workers, and data processing workloads are all supported. We configure the pipeline to match your application runtime — containerised, serverless, or VM-based.
We configure health-check based automated rollback in your deployment tool. If a deployment fails a defined health check within a configurable window, it rolls back automatically without human intervention.
Yes. Every Build tier includes Terraform-based infrastructure-as-code for all three environments (dev, staging, production) with state management, drift detection and code review workflows.
We deploy application performance monitoring, infrastructure metrics, structured log aggregation and on-call alert routing. Dashboards and alert rules are configured for your team from day one.
Yes. The Assess tier documents your current pipeline, identifies gaps and risks, and produces a migration plan. We then migrate incrementally to minimise disruption to ongoing releases.
Managed DevOps covers ongoing pipeline maintenance (library updates, security patches, platform upgrades), monthly health reviews, and incident support with defined response SLAs.
It is a fixed-price engagement that embeds automated security testing into your CI/CD pipeline, deploys a SIEM, applies CIS hardening to your cloud infrastructure, and produces compliance evidence — delivered in 5–7 weeks.
DevSecOps integrates security tooling into the software development pipeline (SAST, DAST, IaC scanning, secrets management). SecOps focuses on detecting and responding to threats in running production systems (SIEM, monitoring, incident response). Our Launchpad covers both.
We provide controls and evidence collection for SOC 2 Type I and II, ISO 27001, and CIS Benchmarks (Cloud and Kubernetes). For regulated industries (healthcare, finance), we can extend to HIPAA and PCI-DSS controls.
Yes. We integrate security scanning into GitHub Actions, GitLab CI, Azure Pipelines, Jenkins, and most other CI/CD platforms. We add scanning stages to your existing pipelines rather than replacing them.
We deploy and configure Microsoft Sentinel, Elastic SIEM, AWS Security Hub, or Splunk Cloud depending on your environment and budget. All configurations include pre-built detection rules and alert triage runbooks.
The SIEM is operational with baseline correlation rules by the end of week 4. You will begin receiving actionable alerts within days of deployment. We tune the rules during week 6–7 to reduce false positives.
We coordinate penetration testing with vetted third-party pen testers as part of the Scale tier or as an add-on to the Build tier. We use the results to validate and improve the controls we deployed.
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.
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.
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.
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.
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.
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.
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.
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.
It is a fixed-price engagement that designs, deploys and automates serverless function workloads on AWS Lambda, Azure Functions or GCP Cloud Functions — with CI/CD, monitoring, secrets management and cost controls included.
Event-driven workloads work best: API backends, file processing, scheduled jobs, webhook handlers, notification pipelines, and data transformation tasks. Workloads that are latency-sensitive or run continuously are better suited to containers or VMs.
AWS Lambda, Azure Functions (Consumption and Premium plans), Google Cloud Functions and Cloud Run, and Knative on Kubernetes for multi-cloud portability.
We analyse your latency requirements and apply appropriate cold start mitigation — provisioned concurrency on AWS Lambda, Premium plan on Azure Functions, minimum instances on Cloud Run — balanced against cost to meet your SLA.
We configure your existing CI/CD platform (GitHub Actions, GitLab CI, Azure Pipelines) to automatically test, package and deploy function changes on merge. Each environment (dev, staging, production) has separate configuration and approval gates.
Secrets are stored in cloud-native secret managers (AWS SSM Parameter Store, Azure Key Vault, GCP Secret Manager) and injected at runtime. We never store credentials in environment variables or function code.
You get a Grafana or cloud-native dashboard showing cost per function, invocation counts, duration percentiles and error rates. We configure budget alerts so you are notified before costs exceed thresholds.
Yes. We frequently inherit existing serverless deployments that lack CI/CD, monitoring or proper secrets management. The Assess tier documents the current state and the Build tier brings it up to production standards.
The VDI Platform Launchpad is a fixed-price, Terraform-automated deployment service that stands up a secure, GPU-accelerated Virtual Desktop Infrastructure on AWS, Azure or Google Cloud in 2–6 weeks.
AWS WorkSpaces and EC2 GPU instances, Microsoft Azure Virtual Desktop (AVD), and Google Cloud Workstations are all supported. We can also run multi-cloud or hybrid configurations.
We deploy and configure Citrix Virtual Apps and Desktops, HP Teradici (PCoIP), and Amazon Nimble Studio depending on your workload requirements, existing licensing, and budget.
Yes. We provision NVIDIA GPU-backed instances (e.g., AWS g4dn, Azure NV-series, GCP A2) and configure the correct driver stack and protocol for graphics-intensive applications including AutoCAD, Unreal Engine, and ML frameworks.
Every deployment includes Zero Trust network segmentation, MFA enforcement, identity-based access policies, session recording, and encryption in transit and at rest. We document all controls for audit and compliance evidence.
You receive full runbook documentation, admin training, and monitoring dashboards. You can self-manage or roll into our Managed tier for ongoing patching, scaling, and 24/5 monitoring support.
Each tier is a fixed-scope, fixed-price engagement confirmed during the free Assessment. Cloud infrastructure costs (compute, storage, licensing) are billed directly to your cloud account and are not included in our fees.
Yes. We help size and procure the right license type (Citrix Universal, Azure AVD multi-session, etc.) and build a total-cost-of-ownership model before you commit to any platform.
We integrate with your existing platform through its APIs and storefront SDK, then add AI capabilities such as search, recommendations, and support automation on top. You keep your current platform.
Yes. We support Shopify, BigCommerce, Magento / Adobe Commerce, WooCommerce, and custom storefronts, and we integrate with common ERP, CRM, and email platforms through their APIs.
Yes. The Commerce AI Launchpad deploys semantic search and a personalized recommendation engine trained on your catalogue and purchase history, with A/B testing to measure lift.
Yes. We build localization-ready, scalable commerce foundations. Some Canadian businesses expanding into new international markets may also be able to explore export-support funding pathways — eligibility is assessed case by case and funding is not guaranteed.
No. Funding is never guaranteed. Programs change frequently and eligibility is assessed case by case. We can help you scope a practical project and identify possible funding pathways to explore with an official advisor.
We are a technology partner, not a funding agency or a licensed funding advisor. We help you scope a fundable project and prepare a clear technical project description. We do not provide legal, tax, or financial advice, and we recommend confirming eligibility with an official program advisor or your accountant.
Projects such as AI adoption, cloud modernization, cybersecurity, data platform modernization, e-commerce and export expansion, productivity automation, custom software and innovation, and industrial or operational modernization may align with certain programs. Whether a specific project qualifies depends on the program, your business, and timing.
Some Canadian businesses expanding into new international markets may be able to explore export-support programs such as CanExport SMEs. Eligibility, funding amounts, and target-market rules are set by the program and change over time, so they must be confirmed directly with the program.
AI adoption, automation, and custom software or innovation projects may align with certain programs or tax-credit pathways, depending on the technical work involved. We help identify possible fit; eligibility is confirmed by the program or your tax advisor.
We review your project idea, identify possible funding categories to explore, and scope a practical, fixed-price project. If it looks like a fit, we point you to the right official advisor and help prepare the technical documentation you may need.
React, Redux, Redux-Saga, RxJS, React-Native, ThreeJS, Angular, Formerly, Vue.js, NuxtJS, Typescript, SASS/SCSS, Apollo GraphQL
Ruby on Rails, MongoDB, Python, Redshift, Django, PostgresSQL, Laravel, Snowflake, .NET, Splunk, MySQL, Talend, Fivetran, Azure SQL Synapse Analytics
React.Js, Typescript, Spring Boot, jQuery, Node.Js, Express.js, Ruby on Rails, Django, GraphQL, Emberjs, Bootstrap, Backbonejs, Angular
TensorFLow, rapidminer, KNIME, PyTorch, Kubeflow, MLflow, MLreef, Kedro, MLrun, ZenML
FIGMA, Adobe XD, Adobe, InDesign, Adobe Photoshop, Adobe Illustrator, CorelDraw, Webflow, Adobe After Effects
SOC2, ISO27001, HIPAA, HITRUST, GDPR, CCPA, CPRA, Crowstrike Falcon, Prisma cloud, Fortigate, Sonarqube, SNYK.io, Docker, Kubernetes, Ansible, Terraform, Lambda, Azure Serverless, Python, Java, .Net, NodeJs
Testing Available: Performance, Compatibility, Cyber Security, Acceptance, Release, Localization, Reliability, Exploratory, Data Warehouse and API
REST, SOAP APIs, Microsoft ASP.net Core Micro Services, Nodejs Micro Services: Loopback, Nest, Moleculer, Sails, Restify
Red Hat, Debian, Kubernetes, JBoss, Oracle Weblogic, Google Cloud, Microsoft Azure, Oracle Cloud, AWS
Tell us about your business and we will help you map the right AI, cloud, data or security project for your goals.
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