── AI integration services ──

AI inside your own systems.

We connect LLMs, agents and ML models to your CRM, ERP, helpdesk and data stack, then roll them out in stages so nothing breaks on the way in.

Start a project →

Free 30-minute scoping call. Written estimate within 5 working days.

Your systems · Your cloud · Staged rollout · Full audit trail

Illustrative interface. Values are sample data.

0+.AI systems in production
0.Days to first deploy · median
0.0%.Uptime · managed inference · 12 mo
0.0×.Cost reduction · fine-tuned vs API · median

* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.

── 01 · AI integration services ──

Six services, one connected system.

AI integration is connecting models, agents and generative features to the software and data you already run, so AI works inside your workflows instead of beside them.

  1. 01

    Integration assessment.

    Which systems, data and workflows AI should touch first, and what each connection needs.

    Deliverable · Integration map
  2. 02

    Solution design.

    Architecture for the AI layer: patterns, gateways, security and fallbacks.

    Deliverable · Target architecture
  3. 03

    Data engineering.

    Pipelines, clean-up and access so models get the right data, with governance built in.

    Deliverable · Production data pipelines
  4. 04

    Custom AI development.

    Models, agents and retrieval built for the workflow, then wired into your stack.

    Deliverable · Integrated AI service
  5. 05

    Generative AI integration.

    LLM features added to the software your teams already use, from CRM to intranet.

    Deliverable · Embedded GenAI feature
  6. 06

    Integration operations.

    Connector monitoring, retries, upgrades and on-call after go-live.

    Deliverable · Monthly health report
── 02 · Integration patterns ──

Four ways AI plugs into your stack.

Most projects use one or two of these. We pick them by where the data lives and where people need the result.

Embedded in your application.

An AI feature inside the screens your users already use, streamed into your UI through an API.

Typical use · Copilot panels, smart form fill

API and event-driven.

Your systems emit events or call an API; the AI step runs and writes results back, with retries and a dead-letter queue.

Typical use · Ticket routing, document intake

Data pipelines.

Models run on a schedule or a stream inside your data platform, and results land where analysts already look.

Typical use · Forecasts, risk scores, tagging

Model runtime.

Models hosted in your own infrastructure behind one gateway, with quotas, logging and rollbacks.

Typical use · Private LLMs, on-prem vision
── 03 · Systems we connect ──

One AI layer, every system you use.

A single gateway sits between your systems and the models, so access, logging and evaluation work the same everywhere.

One AI layer connected to eight kinds of system: CRM and sales, ERP and finance, Helpdesk and ITSM, Data warehouse, Documents, Messaging, Identity and SSO, Custom APIs.CRM and salesERP and financeHelpdesk and ITSMData warehouseDocumentsMessagingIdentity and SSOCustom APIsElluminati AI layerGateway · evals · audit log
Scroll sideways to see the full diagram →

— CRM and sales —

  • Salesforce
  • HubSpot
  • Microsoft Dynamics 365

— ERP and finance —

  • SAP
  • Oracle
  • NetSuite

— Helpdesk and ITSM —

  • Zendesk
  • ServiceNow
  • Jira Service Management
  • Intercom

— Data —

  • Snowflake
  • BigQuery
  • Databricks
  • PostgreSQL

— Documents —

  • SharePoint
  • Google Drive
  • Confluence
  • Box

— Messaging —

  • Slack
  • Microsoft Teams
  • WhatsApp
  • Email

— Identity —

  • Okta
  • Microsoft Entra ID
  • Google Workspace

— Custom —

  • REST
  • GraphQL
  • gRPC
  • Message queues

* Examples of systems we integrate with. Product names belong to their owners; no partnership is implied.

── 04 · Staged rollout ──

Rolled out without disruption.

The AI path earns its traffic. Each stage has a test to pass, and the old path stays one switch away until the numbers settle.

  1. Shadow.

    0% live

    The AI path runs beside your current process on real traffic. Results are compared; nothing changes for users.

    — Move on when —
    Agrees with the current process on 95% of cases for two weeks.

  2. Canary.

    10% live

    A small share of real work goes through the AI path, watched closely, with the old path one switch away.

    — Move on when —
    No rise in errors or complaints, and cost per task within budget.

  3. Ramp.

    50% live

    Half the traffic, including peak hours, so load, latency and cost are proven at scale.

    — Move on when —
    Service levels hold at full daily volume.

  4. Full.

    100% live

    The AI path handles everything. The old path stays available as a rollback while the numbers settle.

    — Move on when —
    One-click rollback kept for 30 days after cut-over.
── 05 · Security and reliability ──

Connected, not exposed.

Integration is where AI meets your most important systems. These controls are part of the design, not a pre-launch checklist.

  1. 01

    Least-privilege access.

    Every connector gets its own service account with only the permissions its job needs, rotated and logged.

  2. 02

    Data stays in your network.

    The AI layer runs in your cloud or data centre. Model providers, where used, are set to zero retention.

  3. 03

    Fallbacks and circuit breakers.

    If a model or a system slows down, work queues or falls back to the manual path. Nothing is lost.

  4. 04

    End-to-end audit trail.

    Every event, model call and write-back is logged with who, what and when, for security and compliance.

  5. 05

    Compliance support.

    Built to support GDPR, HIPAA and SOC 2 controls, with your security team and counsel signing off.

Illustrative interface. Values are sample data.

— Case studies —

The first cohort is being written.

Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?

Talk to us →
── 06 · Industries ──

AI integration for your industry.

Every industry runs on different core systems. The integration patterns and the rollout discipline stay the same.

Mobility.

Dispatch, ETA and rider-support agents for ride-hailing and fleet platforms.

ETA models · Demand forecasting · Support agents

Logistics.

Route optimisation, document extraction and exception handling across the shipment lifecycle.

Route optimisation · Document AI · Exception triage

Finance.

KYC document extraction, fraud signals and customer assistants that stay inside compliance rules.

KYC extraction · Fraud signals · Compliance copilots

Real estate.

Listing enrichment, valuation models and lead-qualifying agents for brokerages and proptech.

Valuation · Listing enrichment · Lead agents

Retail / Manufacturing.

Demand forecasting, visual inspection and inventory copilots on the shop and factory floor.

Forecasting · Visual inspection · Inventory copilots

eCommerce / Consumer goods.

Product search, personalisation and post-purchase support agents that cite the order.

Semantic search · Personalisation · Support agents

Travel.

Itinerary assistants, dynamic pricing and disruption-handling agents for OTAs and operators.

Itinerary agents · Pricing · Disruption handling
— Not listed? —

Let's discuss your industry.

If the data is messy and the stakes are real, the method still applies. Bring the workflow; we'll bring the evaluation set.

Start a project →
── 07 · How we integrate ──

From system map to go-live.

Discovery maps every system the AI will touch before a line of integration code is written.

  1. 01 Discovery · week 1

    Workflow and data audit.

    We map the process, pull a sample of real data, and check what is usable, missing or sensitive.

  2. 02 Discovery · week 2

    Evaluation set.

    We build the test set from your real examples, so “done” has a number before any product code exists.

  3. 03 Build · sprint 1

    Architecture and prototype.

    Model choice, retrieval design and integration plan, proven against the eval set.

  4. 04 Build · every Friday

    Build in the open.

    Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.

  5. 05 Run · go-live

    Deploy.

    A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.

  6. 06 Run · monthly

    Monitor and improve.

    Drift alerts, model upgrades run through your evals, and a monthly cost and quality review.

── 08 · Technology stack ──

The integration stack we use.

Models, orchestration, data and infrastructure, chosen to fit what you already run.

— Models —
  • OpenAI
  • Anthropic
  • Gemini
  • Llama
  • Mistral
  • Qwen
— Agents & orchestration —
  • LangChain
  • Temporal
  • FastAPI
  • Python
— Retrieval & data —
  • pgvector
  • Pinecone
  • Elasticsearch
  • Redis
  • Databricks
  • Snowflake
— Training & serving —
  • PyTorch
  • Hugging Face
  • vLLM
  • NVIDIA TensorRT
  • ONNX
  • OpenCV
— MLOps & observability —
  • MLflow
  • Weights & Biases
  • Grafana
  • Prometheus
  • OpenTelemetry
— Cloud & infrastructure —
  • AWS
  • Google Cloud
  • Azure
  • Kubernetes
  • Terraform
  • Docker

* Tools we build with. No vendor partnership or endorsement is implied.

── 09 · FAQ ──

AI integration questions, answered.

Short answers here. Longer ones on the scoping call.

— Still have a question? —

Message us on WhatsApp. An engineer replies within one working day.

What is AI integration?

AI integration is connecting AI models, agents and generative features to the software and data a business already runs, such as its CRM, ERP, helpdesk and data warehouse, so AI does useful work inside existing workflows instead of in a separate tool.

How much does AI integration cost?

It depends on how many systems are involved, the state of their APIs and data, and the security review. Projects start with a fixed-fee discovery sprint, quoted on the scoping call; the build is priced per milestone, and every quote is written and fixed for its scope.

How long does AI integration take?

After a two-week discovery sprint, integrating AI into one workflow usually takes four to eight weeks, including the staged rollout. Integrations that touch several core systems take longer, and the estimate says so.

Will integrating AI disrupt our current systems?

No. The AI path runs in shadow mode first, beside your current process, then takes a small share of live traffic, then more. The old path stays available as a rollback throughout.

Which systems can you integrate AI with?

Anything with an API, an event stream or a database: CRMs, ERPs, helpdesks, data warehouses, document stores, messaging tools and your own internal services.

How do you keep our data secure during integration?

The AI layer runs in your environment, each connector has least-privilege access, personal data is masked before it reaches a model, and every call is logged. Model providers are configured for zero data retention.

Can you add generative AI to our existing software?

Yes. Most generative AI features we build live inside software a team already uses: a drafting panel in the CRM, a search box on the intranet, or an assistant in Slack or Teams.

Will AI replace our existing automation or RPA?

Not necessarily. AI steps can sit inside the automation you already run. We usually replace only the brittle parts, such as screen scraping or rigid rules that break when documents change.

── Start a project ──

Bring the workflow. We'll bring the plan.

Three steps from first message to kickoff. No deck, no commitment until you sign.

  1. 01Scoping call30 minutes with an engineer, not a salesperson.
  2. 02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
  3. 03KickoffSign the scope and the discovery sprint starts.

Prefer to message directly? WhatsApp +91 7096010005

— Start a pilot —

This is the one that decides whether a pilot is worth doing.

Nothing is stored on this website — the form opens WhatsApp with the message written out, including the pages you looked at, and you press send.

Fixed fee. Two weeks. Starts with a 30-minute call.

Start a pilot →