PranaalikaAI & Big Data Solutions

AI & big data solutions

From raw data to AI your team actually uses

Work directly with founder Adimurthi Adavala on the full path: dependable data, useful AI and the operational foundations that keep both running.

Discuss your project How we work

AI solutions

AI built on your own data

Practical AI that answers questions, writes reports and automates manual work, grounded in your systems rather than the open internet.

  • AI assistants and chat over your data: ask questions in plain language and get answers from your databases and documents.
  • AI report generation: daily, weekly and executive reports drafted automatically from live data.
  • AI agents and workflow automation that take repetitive steps off your team.
  • Document and web data extraction into clean, validated, structured records.
  • Cost-aware model setup: local open-source models where they are good enough, cloud models where they are needed, with automatic fallback.

What we design with you

Start with a workflow, not a model

Your business task
Identify who needs the result, what decisions it supports and where human approval belongs.
Your data boundary
Map the systems to connect, the permissions to preserve and the information that must stay private.
Your success criteria
Agree how to evaluate answer quality, response time, operating cost and failure recovery.
Explore the reference architecture →

Inside the architecture

An answer is useful only if the right person can trust it.

Follow a request through an illustrative AI service. These are design patterns, not a client case study or a promise that one stack fits every project.

  1. Check access

    Identify the caller and the information they are allowed to use.

    Protect private data

  2. Find context

    Retrieve permitted records and relevant documents with source references.

    Ground the answer

  3. Run the workflow

    Call the chosen model and approved tools within defined limits.

    Control what can happen

  4. Return and trace

    Present the result, its sources and uncertainty. Record operational evidence.

    Make results inspectable
Across the whole pathAccess checks on each data/tool callTimeouts and resource limitsRedacted logs and evaluation
Read-oriented request flow. Actions that change a business system need a separate permission and confirmation policy.
For technical teams: the implementation choices behind the flow
Integration boundary
Python APIs and MCP tools expose narrow capabilities. The server enforces access; the model is not the permission system.
Model selection
Choose models using task evaluations, data handling requirements and cost. A fallback must follow the same data policy.
Recovery
Persist long-running work and retry only when repeating it is safe. An uncertain write needs reconciliation, not blind replay.
Explore the system architecture: data, services and models

The request flow above describes the checks. This component view shows where data is prepared, where the AI services run, and how results reach users. Components are illustrative choices, not a required stack.

Reference architecture: AI on your own dataBusiness databases, documents and APIs are ingested and cleaned by pipelines into a trusted data store with vector search. An AI service layer with agents that use tools through MCP, a report generator, guardrails and an audit log, and a model gateway with automatic fallback from local open-source models to cloud LLMs, serves a chat assistant, scheduled reports and alerts to your team.1YOUR DATA2DATA PIPELINES3AI SERVICE LAYER4YOUR TEAMPYTHON · FASTAPIDatabasesERP, CRM, app dataDocuments & filesPDF, Excel, emailApps & APIsSaaS tools, servicesIngest & cleanAirflow, PythonTrusted data storePostgreSQL withvector searchAI agentsUse your tools via MCPReport generatorTemplates + live dataGuardrails & audit logAccess rules, traced answersModel gatewayLocal open-source models,cloud LLMs as fallbackChat assistantAsk in plain languageScheduled reportsDaily, weekly, executiveAlerts & insightsAnomalies flagged earlyRUNS ONDurable workflows: queues, retries, schedulingKubernetes + GitOpsMonitoring: Prometheus, Graylog

Swipe sideways to see the whole diagram.

Reference architecture for client projects, adapted to each system. Kubernetes, GitOps, MCP and the audit layer shown here are design options, selected to fit your access requirements, workload and operating budget.

Big data engineering

Pipelines and platforms that hold up at scale

The foundation for analytics and AI: data that arrives on time, is correct, and lives in one place.

Real-time streaming

Event-driven architectures with Kafka, Azure Event Hubs and stream processing, so data is available in seconds instead of hours.

Batch pipelines & ETL

Scheduled pipelines with Airflow, Python and Talend that bring different sources together, with validation and recovery built into the workflow.

Data lakes & warehouses

Organize raw and curated data for reporting and AI, with PostgreSQL and MySQL integration, clear schemas and access rules.

Quality & monitoring

Automated data quality checks, alerting and dashboards in Grafana, so problems are caught before they reach a report.

Reference architecture: real-time and batch data platform

Reference architecture: real-time and batch data platformLive events, databases and partner files are ingested by Kafka or Azure Event Hubs for streaming and by Airflow or Talend for batch loads. Real-time processing and transformation with quality checks write into a lakehouse with a raw zone and a curated warehouse, which serves dashboards, data APIs and AI and machine learning.1SOURCES2INGEST3PROCESS4STORE5SERVELAKEHOUSEApps & devicesLive events, IoTDatabasesChange data, loadsFiles & partnersCSV, Excel, SFTPStreamingKafka, Event HubsBatch & ETLAirflow, TalendReal-time processingSeconds, not hoursTransform & validateQuality-checked loadsRaw zoneReplayable historyCuratedModelled tablesDashboardsSelf-service BIData APIsApps, partnersAI & MLAssistants, modelsGOVERNANCEData quality checksLineageAccess controlMonitoring & alerts

Swipe sideways to see the whole diagram.

Streaming and batch side by side: real-time data for operations, curated history for reporting and AI.

Cloud & platforms

Cloud-native data platforms you can operate

We move workloads off ageing on-premise systems and build platforms that scale, recover and stay affordable.

  • Cloud migration of data warehouses and pipelines to Azure, planned to avoid downtime.
  • Infrastructure as code with Terraform, so environments are repeatable and reviewable.
  • Containers and Kubernetes for streaming and data services, with auto-scaling.
  • GitOps delivery with GitLab CI/CD, Helm and Flux: every change is reviewed in Git and applied automatically.
  • Observability with Prometheus metrics, Grafana dashboards and Graylog logs.

Technologies we work with

AI & backend
  • Python
  • FastAPI
  • LLM services and AI agents
  • MCP
  • Open-source and cloud LLMs
  • Playwright
  • Pydantic
Streaming & pipelines
  • Apache Kafka
  • Azure Event Hubs
  • Apache Airflow
  • Talend
Storage & warehousing
  • PostgreSQL
  • MySQL
  • Redis
Cloud & operations
  • Azure
  • Terraform
  • Docker
  • Kubernetes & Helm
  • GitOps with Flux
  • GitLab CI/CD
  • Prometheus
  • Grafana
  • Graylog

Reference architecture: cloud platform delivery with GitOps

Reference architecture: cloud platform delivery with GitOpsCode and Helm charts in a Git repository are built, tested and scanned by GitLab CI/CD and pushed as versioned images to a container registry. Flux keeps the Kubernetes cluster in sync with Git. On Azure, provisioned with Terraform, Kubernetes runs the data pipelines and the APIs and AI services, next to managed PostgreSQL and object storage, with Prometheus and Graylog for observability.AZURE · MANAGED WITH TERRAFORMKUBERNETES · HELMGit repositoryCode + Helm chartsGitLab CI/CDBuild, test, scanContainer registryVersioned imagesFlux GitOpsCluster syncs from GitTerraformInfrastructure as codePostgreSQLManaged databaseObject storageFiles, backupsData pipelinesStreaming and batch jobsAPIs & AI servicesFastAPI, LLM agentsObservabilityPrometheus metrics,Graylog logs, alertsdeploys

Swipe sideways to see the whole diagram.

How we ship and run platforms: every change goes through Git and CI, Flux applies it to the cluster, and Terraform keeps the cloud setup reproducible.

Reliability by design

A report backlog should not become your chat assistant’s waiting room.

Separate work by urgency, limit what runs at once, and make recovery explicit. This reference pattern illustrates the decisions—not measured client performance.

Interactive lane

Questions from people

Admission can reserve capacity for time-sensitive requests.

Background lane

Reports and bulk jobs

Work waits in a durable queue instead of creating unlimited workers.

Bounded execution

Claim eligible work within the configured concurrency limit.

  • Question
  • Report
  • Report
  • Free
Example with 4 slots: one is kept for questions from people, reports use the others. Real capacity is sized for the workload.

Failure is a decision

Safe to retry? Save a later start time and release the slot.

Effect uncertain? Stop or reconcile before repeating a write.

The trade-off: reserved capacity versus maximum throughput

A reserve helps protect interactive work but may leave capacity idle. Lending spare slots improves throughput, but the next interactive request may wait for borrowed work to finish. The right policy depends on your latency needs and the limits of downstream services.

Discuss a reliability bottleneck

How we work

Small steps, working software, no surprises

  1. Find the real constraint

    We map the workflow, data access and failure points together.

    You receive: a scoped problem statement and criteria for success.

  2. Make the design testable

    We propose the architecture, trade-offs and a small first milestone.

    You receive: a delivery plan with assumptions, risks and an estimate.

  3. Build and challenge it

    Review working increments, including how the system behaves when a dependency fails.

    You receive: working software and verification against the agreed criteria.

  4. Make it yours to operate

    Agree release and support arrangements before handover.

    You receive: code, deployment guidance, monitoring and recovery notes.

Let's talk about your data

Share what you are trying to achieve. We will suggest a practical first step.

Contact us