# Connect AI to What You Already Run.

Most AI value is unlocked by integration, not by models. Bralak connects intelligent systems to your CRM, ERP, databases and internal tools — with the authentication, error handling and observability that production requires.

## What it is

An AI system that cannot reach your data can only produce suggestions. [The difference between a demo and an operational system](https://www.bralakai.com/insights/prototype-to-production) is almost always integration.

That work is unglamorous and decisive: authentication, rate limits, schema mapping, retries, idempotency, partial failure and observability. Most stalled AI projects stall here, not on model quality.

## Capabilities

- **CRM and ERP integration** — Bidirectional sync with Salesforce, HubSpot, NetSuite, SAP and others
- **API integration** — REST, GraphQL, SOAP and webhooks, with typed contracts and versioning
- **Database and warehouse access** — Direct, read-scoped access to PostgreSQL, MySQL, SQL Server, Snowflake and BigQuery
- **Knowledge base connection** — SharePoint, Drive, Confluence, Notion, wikis, ticket archives
- **Communication platforms** — Slack, Teams, email and SMS as both input and output channels
- **Reliability engineering** — Idempotency, retry with backoff, dead-letter handling and reconciliation

## How it works

The integration layer between an AI system and your systems of record — authentication, mapping, validation, retry and audit.

## The failures that only appear after go-live

Integration is where most AI projects stall, and almost never for the reason the prototype suggested.

- **The API boundary is a contract you do not control** — Fields get added, enums gain values, endpoints deprecate. Responses are parsed into typed internal shapes with explicit handling for the unexpected, so a supplier’s schema change surfaces as a caught and logged error rather than as quietly wrong data three systems downstream.
- **Authentication is what breaks at three in the morning** — Tokens expire, refresh flows fail, service accounts get disabled during an unrelated security review. Refresh is automatic, credential expiry alerts before it lands, and the failure mode is a paused queue rather than a run of rejected writes.
- **Data mapping is where meaning gets lost** — Two systems with a status field rarely mean the same thing by it. The mapping is written down, including what happens to values with no counterpart — dropping those silently is the bug that surfaces a quarter later, during a reconciliation nobody expected to fail.
- **Webhooks arrive twice, out of order, or never** — Delivery is at-least-once at best. Handlers are idempotent, events carry ordering keys where sequence matters, and a reconciliation pass catches what never came — because a missing event is invisible until someone notices a number is wrong.
- **Rate limits are a design constraint, not an error to catch** — A backfill that ignores them gets the whole account throttled, including the path a person is waiting on. Throughput is shaped with queues and concurrency limits, and bulk work is kept off the interactive lane.
- **Partial failure is the normal case in a multi-system write** — Three systems, two succeed. Whether that is compensated, retried or escalated is decided per workflow and written into the design — because “it usually works” is how two systems that both claim to be the source of truth end up disagreeing.

## Use cases

### CRM-connected agent

- **Trigger** — An agent needs account context mid-task
- **Reasoning** — Queries the CRM within the requester’s permissions
- **Action** — Reads and writes back, with every change attributed
- **Result** — Agents work from live data, not a stale copy

### Legacy system access

- **Trigger** — An AI workflow must reach a system with no modern API
- **Reasoning** — Interfaces at the database or file level with validation
- **Action** — Reads and writes safely with a full audit trail
- **Result** — Older systems stop blocking the project

### Multi-system reconciliation

- **Trigger** — The same record exists in several systems
- **Reasoning** — Compares, identifies the authoritative source per field
- **Action** — Reconciles or flags conflicts for review
- **Result** — Data disagreements surface before they compound

### Event-driven triggering

- **Trigger** — A business event occurs anywhere
- **Reasoning** — Normalises the event and determines which workflow applies
- **Action** — Triggers it with the full context attached
- **Result** — Workflows start on reality, not on a schedule

## Integrations

- Salesforce
- HubSpot
- NetSuite
- SAP
- Dynamics
- Zendesk
- Intercom
- Shopify
- Stripe
- Snowflake
- BigQuery
- PostgreSQL
- SQL Server
- SharePoint
- Slack
- Teams

## Technologies

- Python
- FastAPI
- Node.js
- TypeScript
- PostgreSQL
- Redis
- Docker
- OpenTelemetry

## How we work

Five phases, each ending in a decision you make: Discover → Architect → Prototype → Production → Optimize.

Full process: [How we work](https://www.bralakai.com/how-we-work).

## FAQs

### What if our system has no API?

We integrate at the database, file or scheduled-export level, with the same validation and audit as an API integration. Very few systems are genuinely unreachable.

### How do you handle credentials?

Environment-scoped secrets, never in client code, with least-privilege service accounts and rotation. Access scope is agreed in writing before implementation.

### What happens when a system is down?

Queued with retry and backoff; anything unrecoverable lands in a dead-letter queue with an alert. Work is not silently lost.

### Will this slow our existing systems?

Integrations are rate-limited and, where volume warrants, read from a replica. Load impact is agreed before deployment.

### Can you work with our security team?

Yes, and it goes better when we do. Architecture and data-flow review before implementation avoids reworking a finished system.

## Related

- [AI Agent Development](https://www.bralakai.com/ai-agent-development) — Agents that reason through a problem, call your systems and complete the work end to end.
- [AI Automation](https://www.bralakai.com/ai-automation) — Workflows that read unstructured input, apply judgement and route the exceptions to people.
- [RAG Development](https://www.bralakai.com/rag-development) — Retrieval-augmented generation over your own documentation, with citations and permission-aware access.
- [AI Consulting](https://www.bralakai.com/ai-consulting) — A structured path from business problem to a working system, chosen on evidence rather than ambition.
- [Logistics (industry)](https://www.bralakai.com/industries/logistics)
- [FinTech (industry)](https://www.bralakai.com/industries/fintech)

## Your Next Intelligent System Starts Here.

Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path.

- [Book an AI Strategy Call](https://www.bralakai.com/contact)
- [Start a Project](https://www.bralakai.com/contact)

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*Bralak AI — Building Intelligent Solutions · Automating the Future.* Bralak AI Pvt. Ltd. — Noida, UP, India.

- Canonical page: https://www.bralakai.com/ai-integration
- Agent index: https://www.bralakai.com/llms.txt · full text: https://www.bralakai.com/llms-full.txt
- Contact: info@bralakai.com · https://www.bralakai.com/contact