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INTELLIA

Artificial Intelligence

AI that does real work inside your business.

RAG assistants, AI agents, document intelligence and predictive models, connected to your systems and measured against business outcomes.

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AI & Machine Learning Development at INTELLIA, in brief

INTELLIA builds production AI for businesses: retrieval-augmented (RAG) assistants that answer from your own documents, AI agents that complete tasks in your systems, document extraction, and machine-learning models for forecasting and classification. Each project starts with a measurable use case, a small pilot on real data, and an evaluation plan before scaling.

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Why most AI pilots never reach production

Generative AI demos are easy. Systems your staff trust every day are not. Pilots stall because they were built on sample data, nobody defined what a good answer looks like, the model cannot see the systems where work actually happens, and there is no plan for cost, privacy or monitoring.

  • Answers that sound right but are not grounded in your documents
  • No evaluation set, so quality cannot be measured or improved
  • AI that sits in a separate chat window instead of inside CRM, ERP or WhatsApp
  • Unclear data-residency and privacy position for customer data
  • Token costs that grow faster than usage

Capabilities

What our AI & ML team delivers

  • RAG assistants and knowledge search

    Assistants that answer from your policies, manuals, tickets and contracts, with citations back to the source passage.

  • AI agents and workflow automation

    Agents that call your APIs to check orders, book appointments, raise tickets or update records, with human approval where it matters.

  • Document intelligence

    Extract fields from invoices, IDs, forms and contracts into structured data, with confidence scores and review queues.

  • Conversational AI on WhatsApp and web

    Customer-facing assistants on the WhatsApp Business Platform, website chat and contact-center channels, with handover to agents.

  • Predictive and classification models

    Demand forecasting, churn and risk scoring, ticket routing and anomaly detection using classical machine learning where it beats an LLM.

  • Evaluation, guardrails and LLMOps

    Test sets, automated scoring, prompt and model versioning, PII redaction, cost tracking and monitoring in production.

Our approach

How a typical engagement runs

  1. 1

    Pick one measurable use case

    We agree on a single workflow, a baseline metric (for example time to answer, or tickets deflected) and a target.

  2. 2

    Assemble and clean the knowledge

    We inventory sources, set access rules, chunk and index documents, and build an evaluation set of real questions with correct answers.

  3. 3

    Pilot on real data

    A working assistant or model, used by a small group for two to four weeks, scored automatically and by your experts.

  4. 4

    Integrate where work happens

    Connect to CRM, ERP, helpdesk, WhatsApp or intranet with role-based access and audit logs.

  5. 5

    Operate and improve

    Dashboards for quality, latency and cost; monthly review of failure cases; model and prompt updates under version control.

When should a business use AI, and when should it not?

Use AI when the task involves reading, writing, classifying or searching unstructured information at volume, and when a wrong answer can be caught or corrected. Do not use it when a simple rule or report would do the job, or when every answer must be exactly right with no human review.

A useful test is to ask three questions about a candidate workflow:

  1. Is there a lot of text, speech or images involved? Support tickets, policy documents, call recordings and scanned forms are good candidates.
  2. Is the work repetitive but slightly different each time? That is where models outperform hard-coded rules.
  3. Can we measure success? If nobody can say what a correct outcome looks like, the project will drift.

RAG assistant, fine-tuning or classical machine learning?

Most business AI projects fall into one of three technical patterns. Choosing the right one first saves months.

Pattern Best for Data needed Typical trade-off
RAG assistant Answering questions from documents, policies, product data Your existing documents, kept current Quality depends on document quality and retrieval tuning
Fine-tuned model Consistent tone, format or domain vocabulary Hundreds to thousands of labeled examples Higher upfront effort; must be retrained as knowledge changes
Classical ML Forecasting, scoring, routing, anomaly detection Historical structured data with outcomes Cheap to run and explainable, but needs clean history

We often combine them. A support assistant might use RAG for answers, a small classifier to route tickets, and an agent step to look up order status.

What does an AI agent actually do?

An AI agent is a model that can decide to call tools: look up a customer, check stock, create a ticket, schedule a visit. The model plans the steps, our code executes them with the same permissions a human user would have, and every action is logged.

For anything that changes money, contracts or customer records, we design the agent to propose the action and wait for human approval. That keeps speed where it is safe and control where it is needed.

How we keep AI safe in production

  • Grounding and citations. Answers link to the passage they came from, so staff can verify quickly.
  • Access control. The assistant only retrieves documents the signed-in user is allowed to see.
  • PII handling. Personal data is masked before it is sent to external models where policy requires it.
  • Evaluation before every release. Prompt or model changes must pass the evaluation set.
  • Monitoring. We track answer quality signals, latency, cost per interaction and escalation rates.

How this connects to the rest of your stack

AI is most useful inside tools people already use. We regularly connect assistants to CX Hub for support teams, to LOOMX for WhatsApp conversations, and to SCRIBA for document search. For the data foundations underneath, see our data and analytics service.

For a deeper technical walkthrough, read our guide to building an AI support agent with RAG.

Technologies we use

  • OpenAI, Anthropic and open-weight models
  • Azure OpenAI Service
  • LangChain and LlamaIndex
  • PostgreSQL pgvector, Qdrant
  • Python, FastAPI
  • scikit-learn, XGBoost
  • OCR and layout models
  • Meta WhatsApp Business Platform

Related INTELLIA products

Related case studies

Pricing and engagement

How we work together

AI work carries more uncertainty than conventional software, so we stage the commitment. You decide whether to scale after seeing pilot results on your own data.

  • Discovery and feasibility

    Best for: Teams with several AI ideas

    Short, fixed-price engagement that ranks use cases by value and feasibility and tests the top one on a data sample.

  • Pilot to production

    Best for: One defined use case

    Fixed-scope build of a pilot, followed by a priced production phase once evaluation targets are met.

  • AI squad

    Best for: A roadmap of AI features

    A dedicated team of ML engineers, a backend engineer and a QA analyst working through your backlog monthly.

We do not publish fixed prices because scope drives cost. Every proposal states assumptions, inclusions and exclusions.

FAQ

AI & ML: common questions

What is retrieval-augmented generation (RAG)?

RAG is a pattern where an AI model first searches your own documents for relevant passages and then writes its answer using only those passages. It reduces made-up answers, lets the assistant cite sources, and means you update knowledge by updating documents rather than retraining a model.

Will our data be used to train public AI models?

No. We use enterprise API terms that exclude customer data from model training, or open-weight models hosted in your own cloud tenancy or on-premises. Data flows, retention and regions are documented before the pilot starts.

How long does an AI pilot take?

Most pilots take four to eight weeks: one to two weeks to prepare data and the evaluation set, two to four weeks to build, and two weeks of use by a pilot group. Timelines depend on how ready and accessible your data is.

How do you measure whether the AI is good enough?

We build an evaluation set of real questions or documents with known correct outcomes, score every model and prompt version against it automatically, and add expert review for edge cases. Go-live criteria are agreed in advance.

Can the AI work in Urdu or Arabic as well as English?

Yes. Current large language models handle Urdu, Roman Urdu and Arabic well for most business tasks. We test quality in each language your users need and tune prompts or retrieval for mixed-language queries.

How much does an AI project cost to run each month?

Running cost depends on volume, model choice and context size. We estimate cost per conversation or per document during the pilot, and use caching, smaller models for simple steps and retrieval limits to keep it predictable.

Do we need a data science team to maintain it?

No. We hand over dashboards, runbooks and an admin panel for updating knowledge sources. Many clients keep us on managed support for model updates and monthly quality reviews.

Further reading

Planning a AI & ML project?

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