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Praval Technologies

Digital Engineering

Generative & Agentic AI

Agents that own the workflow. Not another chatbot that answers one question.

12+

capabilities shipped into production systems

We design, build and operate generative and agentic AI systems: reasoning, tool-use and multi-agent orchestration wired directly into the platforms you already run on. Every agent ships with a measured baseline, guardrails and an evaluation harness, so what goes live keeps working after the first demo ends.

AI Agent Development Services

We design agents that ship. Then we keep them shipping.

From the first conversation about which workflow is costing you hours, to the agent running in production with observability and guardrails: four services, one senior team from kickoff to year-three optimization.

Strategic AI Agent Consultation

We start with the workflow, not the model. Where are the hours leaking: missed follow-ups, manual data entry, support triage? We map which agents to build, in what order, and what ROI to expect before a line of code.

Output

  • Agent roadmap
  • ROI model
  • Framework recommendation

Bespoke Agent Design & Development

Custom-built from the architecture up: how the agent thinks, which tools it uses, how it handles errors. Multi-step reasoning, tool-use and multi-agent orchestration designed for the real task, not a demo.

Built on

  • LangGraph
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • MCP

Agent System Integration

Agents slide into the stack you already run: CRMs, helpdesks, ERPs, project tools, calendars and data warehouses. Auth, permissions, audit trails and rate limits handled up front, so nothing breaks when the agent goes live.

Connects to

  • Microsoft 365
  • Salesforce
  • ServiceNow
  • SAP
  • Snowflake
  • Oracle

Ongoing Optimization, Training & Support

Agents drift. Workflows change. We stay on: performance reviews, prompt tuning, retraining on new data, error logging and cost-drift monitoring. Available around the clock when the agent is the one running production.

Delivered via

  • Weekly KPI reviews
  • 24/7 monitoring
  • Quarterly retraining

Generative AI Development Services

Our Generative AI development services

Models are the easy part. We build the application, the data pipeline and the evaluation harness around them, so what ships keeps working after the first demo ends.

  • Generative AI Application Development

    Custom AI-powered web and mobile apps that generate dynamic, personalised content, built on Azure OpenAI, Anthropic Claude, or open-weight Llama where the economics favour it.

  • Generative AI Consulting

    Our consultants start with your business needs, data and goals, then determine which generative AI techniques and frameworks will actually reach the result you asked for. Including where a custom build is not the right answer.

  • Model Fine-tuning

    We fine-tune pre-trained models to your domain and use case. Using your data, we lift accuracy, quality and performance, and measure the gain against a baseline before it goes anywhere near production.

Integration

Generative AI Integration Services

We deploy custom generative AI models as APIs or microservices and integrate them into the applications and platforms you already run, so the capability shows up at the point of work rather than in a separate tool nobody opens.

  • Deployed as versioned APIs or containerised microservices
  • Wired into your existing apps, portals and Microsoft 365 estate
  • Auth, rate limits and cost controls handled before go-live

Plus three generative AI capabilities we offer

  1. 01

    Data Preparation and Training

    We help you organise, clean and structure your data so it is genuinely ready to train against: the step that decides whether the model that follows is any good.

  2. 02

    Custom Generative AI Model Development

    We design and build models from scratch where an off-the-shelf model cannot meet the requirement, aligned to your outputs rather than to a template.

  3. 03

    Generative AI Monitoring and Maintenance

    Continuous monitoring and evaluation after go-live. We identify degradation, bias and drift, and ship the updates that keep the system reliable.

How we build

Two disciplines. Twelve capabilities production actually needs.

Generative AI produces the answer. Agentic AI decides what to do with it and acts. Models stand up in an afternoon; systems that survive contact with production are a different animal. These are the disciplines we reach for when the stakes are a workflow your team depends on.

  • Custom LLM & fine-tuning

    Domain-tuned models on your proprietary data, evaluated against a measured baseline before rollout, not swapped in on faith.

    • Azure OpenAI
    • Claude
    • Llama
    • LoRA

  • RAG & knowledge retrieval

    Retrieval-grounded assistants over your own documents, wikis and case history, with citations, not confident guesses.

    • Azure AI Search
    • pgvector
    • LlamaIndex
    • Qdrant

  • Document intelligence

    Extraction, classification and validation across contracts, forms and unstructured PDFs, with confidence scoring built in.

    • Document Intelligence
    • OCR
    • Schema validation

  • Content & summarization

    Drafting, summarizing and rewriting at the point of work (briefs, case notes, proposals), reviewed by the person who owns the output.

    • Prompt templates
    • Microsoft Graph
    • Word & Outlook

  • Conversational analytics

    Ask a question in plain language, get a chart and the query that produced it, every step auditable, nothing black-boxed.

    • NL2SQL
    • NL2DAX
    • Power BI
    • Fabric

  • Monitoring & drift detection

    Continuous evaluation for hallucination, bias and quality drift after go-live: degradation caught before your users notice.

    • Ragas
    • AI Foundry evals
    • App Insights

Every build draws from both tracks as the task needs: a document-intelligence pipeline is generative until an agent decides what to file, flag or forward next.

How we engage

Find the stage that matches where you are

Four ways to work with us, from a first strategy conversation to an agent running in production and under review.

  1. Stage: Exploration

    What you’re asking: “Should we build an agent, and which one first?”

    Our service: Strategic AI Agent Consultation

    Timeline: 2–3 weeks

  2. Stage: Prototype

    What you’re asking: “Build a working agent on our real data.”

    Our service: Agent Design & Development

    Timeline: 6–10 weeks

  3. Stage: Production

    What you’re asking: “Integrate the agent with our CRM, ERP and Microsoft estate.”

    Our service: AI System Integration

    Timeline: 8–12 weeks

  4. Stage: Scale

    What you’re asking: “Keep it tuned, observed and improving in production.”

    Our service: Ongoing Optimization & Support

    Timeline: Continuous

Platforms

Microsoft & AzureSnowflakeServiceNowSalesforce

Technologies

Microsoft FabricAzure Data ServicesMicrosoft Power Platform

Industries

Healthcare & Life SciencesManufacturing & Supply ChainLogistics & TransportationRetail

Where it applies

Industries we build generative and agentic AI for

The core discipline stays the same across every deployment. What changes is the data realities, the regulation, and the systems already in place.

Platform focus

Deep in the Microsoft estate you already run.

A large share of the workflows we automate already happen in Teams, Outlook and SharePoint. We build agents that meet people there: inside Microsoft Teams as a bot or Copilot extension, inside Power Automate as an orchestration step, and inside the Microsoft Graph as the connective layer to mail, calendar and files.

  • Microsoft Teams
  • Microsoft 365 Copilot
  • Power Platform
  • Azure OpenAI Service
  • Microsoft Graph
  • SharePoint

  • Agents inside Teams

    Deployed as a Teams bot or Copilot extension, so the agent sits where the conversation already happens.

  • Azure-native architecture

    Built on Azure OpenAI Service and Azure infrastructure, aligned with the governance your security team already trusts.

  • Beyond Microsoft

    The same integration discipline extends to Salesforce, ServiceNow, Snowflake and the estates we already support.

Start with the problem

Tell us which workflow is costing you hours. We’ll tell you honestly whether an agent is the right fix.

No slide deck of use cases. A working point of view on your problem, from a senior team.

See how we engage

Related

Related work

3 of 3 shown

Service

AI & Machine Learning

Predictive, classification and computer-vision models built on your own data, each with a measured baseline and an honest read on where it breaks.

Service

ML Ops & Governance

The layer between a model that works and a model that keeps working: deployment pipelines, feature parity, drift monitoring, retraining gates and rollback.

Platform

Microsoft & Azure

Azure consulting, migration, managed services and governance, plus the Microsoft data and low-code estate: Fabric, Power BI and Power Platform.

Questions

Common questions

How do you know an agent build actually worked?
We measure the baseline before we build. If we cannot state the current handling time, deflection rate or error rate, we are not ready to start, and neither is the agent.
What's the difference between an agent and a chatbot?
A chatbot answers a question. An agent plans, calls tools, and completes a multi-step task without being prompted at every turn, then hands off to a person the moment the case leaves its policy.
Do agents replace the CRM, ERP or helpdesk we already use?
No, and they should not try to. Agents sit on top of the systems you already run as an intelligent layer, reading data via API and triggering actions inside them, not replacing them.
How do you keep agents from taking an unsafe action?
Permission boundaries, human approval checkpoints, audit logging and kill-switches, layered from the first sprint. Agents interact with your systems autonomously, so we monitor them as the privileged users they are.

Why Praval

How we work with you.

Industry expertise

Seasoned professionals with deep industry knowledge and hands-on experience driving digital acceleration across sectors.

Client-centric approach

We prioritise understanding your challenges, goals and culture, and deliver solutions tailored to them rather than to a template.

Proven methodologies

Industry-leading frameworks and best practice, giving a structured and repeatable route to the outcome you asked for.

Collaborative partnership

We work as an extension of your organisation: transparency and agility during the engagement, and a handoff that holds after it.

  1. 01

    Initial consultation

    We evaluate your current systems and identify where the value is.

  2. 02

    Customized plan

    We design a solution scoped to your business, not to a template.

  3. 03

    Design & development

    We build and transition with minimal disruption to live operations.

  4. 04

    Monitoring & support

    Continuous oversight and support keep the estate healthy afterwards.