What solutions do Innovatics offer?

Innovatics offers a suite of intelligence solutions, each designed for a specific business function and industry. Each solution combines data analytics, AI modeling, workflow automation, and human expert support to deliver decision intelligence configured for your business.

Does Innovatics build industry-specific AI solutions?

Yes, where the decision problem demands it. While our core programs are function-specific, we configure each deployment to reflect the commercial realities of your industry.

Which industries do Innovatics serve?

Innovatics works across retail, e-commerce, and real estate. Our programs are built around function-specific decision problems — marketing, revenue ops, operations, sales.

What kind of companies benefit most from Innovatics?

Mid-market and growth-stage companies that already have data but lack the internal capability to turn it into consistent, actionable intelligence. If your team spends more time building reports than acting on them, the Innovatics solution is the answer to that.

Who is the Innovatics partnership program designed for?

The Innovatics partnership program invites fractional Chief Marketing Officers and Chief Revenue Officers to collaborate with us and leverage our tech capabilities and AI expertise to drive revenue and growth of their clients.

Capabilities · Technology · Applied AI

Applied AI, built for the work in front of you.

We work across the full applied AI stack. From LLM integration and fine-tuning open-source foundations, to traditional ML, forecasting, optimization, and the engineering layer that makes models reliable in production.

Start a working session See what we build

02 · What we build

An applied AI practice across three dimensions.

Our work spans language and reasoning systems, predictive and decision systems, and the engineering layer that makes models reliable in production. The capabilities below are the ones we ship most often.

Theme 01

Language & reasoning systems

The systems that can read, write, classify, and reason over language. It is built across closed-source LLMs, open-source foundations, and language models that are tuned to specific domains.

LLM integration across providers

Production systems built on Claude, GPT, and Gemini. We integrate across providers and choose the model that fits the use case, long-context reasoning, low-latency classification, and multi-step workflows.

Small Language Models for narrow domains

Small Language Models trained or specialized for a specific domain. When the task is well-defined, SLMs often deliver better cost, latency, and accuracy than larger general-purpose models.

Fine-tuning open-source foundations

Fine-tuning Llama, Mistral, and other open-source families on client-specific data. The right lever when behavior needs to be shaped to a domain, and when the model needs to stay inside the client's walls.

NLP beyond LLMs

Classical NLP works where it's the right tool, entity extraction, topic modelling, classification, and classical sentiment analysis. Fast, deterministic, well-suited for high-volume processing pipelines.

Theme 02

Predictive & decision systems

The systems that forecast, classify, recommend, and optimize, the foundational ML and decision-intelligence work and run underneath our engagements.

Traditional ML

Classification, regression, clustering, and recommendation systems. The foundational machine learning work that delivers predictive value across customer behavior, demand patterns, risk, and operational decisions.

Time-series forecasting

Demand forecasting, capacity planning, inventory prediction, and financial projection. We build forecasts that drive real planning decisions, not point estimates that sit in a dashboard.

Multi-modal AI

Systems that combine text, images, and audio in a single reasoning layer. Useful when the signal lives across formats, a document with diagrams, a call transcript with sentiment, and a product with spec details and photo, etc.

Optimisation & mathematical programming

Operations research and mathematical optimization for scheduling, routing, allocation, and constrained decision problems. The disciplined alternative when ML alone isn't the right tool.

Theme 03

Engineering & operations

The layer that makes models reliable in production i.e., deployment posture, model lifecycle, evaluation, and the operational craft that keeps systems healthy after launch.

Deployment inside your infrastructure

Production AI workloads shipped inside customer VPCs, on-prem GPUs, and sovereign cloud regions. For clients with data residency, IP, or regulatory requirements, the model stays on their side of the wall.

MLOps with NexML

NexML is our internal MLOps accelerator, a methodology and tooling layer built across multiple client engagements for taking models from notebook to production. It encodes the patterns we use for data preparation, evaluation, monitoring, retraining cadence, and rollback.

AI evaluation & observability

The work that answers "is the model actually doing what we think it's doing?", evaluation pipelines, drift detection, output quality monitoring, and post-deployment instrumentation that make AI systems trustworthy over time.

03 · How we think about it

Three things we believe about AI engineering.

Belief 01

Model choice is a problem choice, not a vendor choice.

We work across closed-source LLMs, open-source foundations, and Small Language Models, and we pick them by the work, not by the partnership. Sometimes the right answer is a frontier model. Sometimes it's a fine-tuned 7B parameter model running on your hardware. The choice follows the problem.

Belief 02

Where the model runs matters as much as which model it is.

Cloud-API consumption works for many engagements. For regulated industries, data residency requirements, or IP-sensitive workloads, the model needs to run inside the client's walls. We deploy where the work needs to land, customer VPC, on-prem GPUs, sovereign cloud regions. Different surfaces have different operational requirements; we have shipped to all of them.

Belief 03

The model is the easiest part.

Most of the value, and most of the failure modes, live in the pipeline around the model. Data preparation. Evaluation. Drift monitoring. Cost control. We treat the model as onecomponent in a system , not as the system itself. The MLOps work is where most of our delivery time actually goes.

04 · Tools and ecosystem

The tools we work with.

The models, frameworks, and platforms we have used to ship production AI/ML work, named honestly across closed-source, open-source, and cloud.

Models

ClaudeGPTGeminiLlamaMistral

Closed-source LLMs alongside open-source families we fine-tune.

Languages & Frameworks

PythonPyTorchHugging FaceLangChainMLflow

Standard ML engineering stack, deployed in client environments.

Data & Infrastructure

SnowflakeDatabricksdbtAWSAzureGCP

Cross-cloud, cross-platform — we deploy where the work lives.

Strategic Partnerships

Snowflakedbt

05 · FAQ

Questions a Head of Data usually asks first.

Four things we get asked early in technical conversations. We have put the honest answers here so you can decide whether a working session is worth your time.

01How do you decide which LLM to use for a given engagement?+

The choice starts with the problem. For long-context reasoning we lean one way. For low-latency classification, we lean on another. Cost, latency, accuracy, and deployment constraints all shape the answer. We have built systems on Claude, GPT, Gemini, and fine-tuned open-source, in the working session we will explain which one fits your work and why.

02Can you deploy models inside our own infrastructure?+

Yes. We have shipped production AI workloads inside customer VPCs, on customer-owned GPUs, and in sovereign cloud regions. If the requirement is that the model never leaves your perimeter, for regulatory, IP, or data-residency reasons, that's deployment work we have done. The exact target shapes the deployment plan.

03When does fine-tuning make sense versus prompting a frontier model?+

Frontier models with good prompts go further than most teams expect. Fine-tuning makes sense when you need themodel's behavior shaped to a narrow domain , when you need it running inside your infrastructure, or when you need lower inference cost at scale.

04What does production AI/ML actually involve beyond the model?+

Most of the production work lives outside the model itself, data preparation, evaluation, drift monitoring, retraining cadence, cost control, and rollback paths. We treat the model as one component in a system. The MLOps work is where most of our delivery time actually goes.

Most AI consulting picks a vendor and shapes the problem to fit. We pick the model after we understand the problem.

07 · Ready when you are

Tell us what you are trying to build. We will tell you what we would actually use.

A working session with a senior engineer, 30 to 45 minutes, focused on your problem, your data, and your deployment constraints. No commitment. We leave you with useful thinking either way.