Practical AI, Built Into How Your Business Already Works
AI/ML consulting, generative AI and LLM integration, chatbots, computer vision, and workflow automation, built for real business use cases.
What We Offer
AI Solutions Services
Our Process
How We Deliver
Use-Case Discovery & Feasibility Scoping
Stakeholder workshops to surface candidate AI use cases, followed by a feasibility and ROI pass on each — data availability, technical risk, and expected impact — so the first project chosen is the one most likely to work, not the most talked-about one.
Data Audit & Readiness Assessment
An honest look at the data actually available for the chosen use case — volume, quality, labeling status, and access — before any model or integration work is scoped, since most AI project delays trace back to data gaps discovered too late.
Solution Design & Proof of Concept
A working proof of concept built against real (not synthetic) sample data, using the smallest architecture that can prove the concept — an off-the-shelf LLM API, a lightweight fine-tuned model, or a rules-plus-ML hybrid — before committing to a larger build.
Development & System Integration
Full build-out once the POC is validated: model or pipeline development, integration with existing CRMs, ERPs, helpdesks, or internal databases, and the middleware needed to connect AI components to the systems your team already uses daily.
Testing, Evaluation & Responsible AI Review
Structured evaluation against accuracy, latency, and cost benchmarks, plus a responsible-AI pass — bias checks, hallucination/error-rate testing for generative components, and data-handling review — before anything reaches production users.
Deployment, Monitoring & Continuous Improvement
Production rollout with monitoring for model drift, output quality, and usage patterns, followed by a defined retraining or prompt-refinement cadence so the solution keeps performing as real-world data and usage evolve past launch.
Our Toolkit
Technologies We Work With
Why Choose Agilux
- Every engagement starts with a feasibility and data-readiness assessment before a build is scoped — if the data isn't there yet, we say so, rather than starting a project set up to underdeliver.
- Provider-agnostic architecture — OpenAI, Anthropic, Google Gemini, AWS Bedrock, or open-source models (Llama, Mistral) selected per use case based on cost, data sensitivity, and latency, not locked to a single vendor by default.
- Proof-of-concept before full build on any generative AI or custom ML engagement, so you see real output against your own data before committing to the larger scope.
- Responsible AI built into the delivery process, not bolted on afterward — bias checks, explainability review, and data-handling documentation are a standard project step, not a separate add-on service.
- MLOps and monitoring included in scope for custom model work, so deployed models are tracked for drift and retrained on a defined cadence instead of degrading silently after launch.
- One team spanning data engineering, ML/AI development, and application integration, so an AI feature actually lands inside your existing systems instead of living as a disconnected pilot.
Industries We Serve
What Results Typically Look Like
- Businesses that automate document-heavy or repetitive support workflows with AI typically see meaningful reductions in manual processing time and turnaround for those specific tasks, though the actual percentage depends heavily on document/query complexity and current process maturity — we size this per use case rather than quoting a blanket figure.
- AI chatbot and support-automation deployments generally deflect a portion of routine, repeatable queries away from human agents, freeing support teams to focus on complex cases — the deflection rate varies widely by query mix and knowledge-base quality, and is something we'd benchmark against your own ticket data, not assume upfront.
- Predictive analytics and forecasting models tend to improve planning accuracy over spreadsheet-based or gut-feel forecasting once enough historical data is available, but the realistic uplift for your business — [X]% forecast error reduction, [Y] hours saved per week — is best established with a baseline measurement before the project starts, which we build into the discovery phase.
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