AI integration services: senior teams that wire AI into your systems
Senior engineers connect models to the systems you already run, with gateways, access controls, fallbacks and monitoring, so AI works inside real workflows.
By the Ryz Labs team · Updated October 2026
Ryz Labs provides AI integration services through senior engineers and dedicated AI pods who connect language models and ML models to the systems you already run: CRM, ERP, ticketing, document stores, data warehouses and internal APIs. The work covers the gateway, authentication, data handling, fallbacks and monitoring that make a model usable in production. Our engineers are the top 1% of the tens of thousands we have interviewed and work on US business hours.
What we build
AI integration is mostly systems engineering. The model is one dependency among many, and usually the least predictable one. Typical work:
- Model gateways. One internal service that every application calls, handling provider keys, routing, retries, rate limits, logging and cost attribution per team.
- CRM and service desk integrations. Summaries, suggested replies and classification inside Salesforce, ServiceNow, Zendesk or your own tools, written back through their APIs.
- Event-driven enrichment. Queue consumers that classify or extract from new records as they arrive, so results are ready before anyone opens the screen.
- Document and knowledge connectors. Sync jobs from SharePoint, Confluence, Google Drive or S3 into a search index, keeping permissions attached.
- Data platform integrations. Model calls inside Snowflake, Databricks or warehouse pipelines for bulk enrichment, with outputs versioned and traceable.
- Private model access. Network setup for AWS Bedrock or Azure OpenAI through private endpoints in your accounts, with identity-based access instead of shared keys.
- Agent tool access. MCP servers and typed APIs that let agents use internal systems with scoped permissions.
How an engagement works
- Talk. We map the target workflow and the systems on either side of the model: where inputs come from, where outputs land and who owns each system.
- Match. We propose engineers who know your platforms, for example backend engineers with Salesforce or Azure experience plus an AI engineer, with names and a price.
- Join. The team works in your repos, CI and cloud accounts, follows your change management and demos each integration on real records.
- Grow. Reuse the gateway and patterns for the next workflow, or hand the integration and runbooks to your platform team.
In week 1, the team gets sandbox access to each system, confirms API limits and data classifications, and documents the data flow for your security team. By month 1, the first integration typically runs end to end in staging with logging and an evaluation set. By month 3, typical work is production rollout, alerting, cost reports and a second workflow on the same foundation. Scope and access determine the pace.
The stack our teams work in
| Layer | Tools we use | Notes |
|---|
| Model access | AWS Bedrock, Azure OpenAI, Anthropic API, OpenAI API | Private endpoints and IAM or Entra ID auth where your policy requires it. |
| Gateway | LiteLLM, AWS API Gateway, Azure API Management, custom services | Central place for keys, quotas, logging and fallbacks. |
| Business systems | Salesforce, ServiceNow, Zendesk, SharePoint, Microsoft 365 and Slack APIs | Respecting each system's API limits and permission model. |
| Messaging | SQS, SNS, EventBridge, Azure Service Bus, Kafka | Asynchronous calls so slow model responses do not block users. |
| Data | Postgres, pgvector, Snowflake, Databricks, OpenSearch | Outputs stored with model version and prompt version. |
| Security and observability | Microsoft Presidio for PII, AWS KMS, Azure Key Vault, OpenTelemetry, Datadog | Redaction before logging; traces across system boundaries. |
How we keep AI integrations dependable
Integrations fail at the seams between systems. The issues a senior team plans for:
- Provider outages and throttling. Model APIs return 429s and 5xx errors. The gateway retries with backoff, fails over to a second deployment or provider where you approve it, and degrades gracefully when both are down.
- Latency in user-facing paths. Model calls can take seconds. We stream where users wait, precompute where they do not, and set timeouts that match the screen.
- Malformed outputs breaking downstream systems. Every response is validated against a schema before it is written to a CRM field or a database.
- Permission leaks. A model that sees a document can repeat it. Retrieval and context assembly filter by the requesting user's access, not a service account's.
- Sensitive data in logs. Prompts often contain customer data. PII is redacted before logging, and retention follows your policy.
- Silent behavior changes. Model versions are pinned, and an eval suite runs before any upgrade or prompt change ships.
- Unowned cost. Every call is tagged by application and team, so finance can see where spend comes from and set budgets.
Our pods have done this integration work on production systems such as fraud detection for a global fleet company, where each record is scored in under 30 seconds and $5.94M in fraud was confirmed by the client's fraud team. See the case studies.
Team shapes and cost
Ryz engineers typically cost $7,000 to $15,000 per engineer per month: mid-level (comparable to Amazon L5) $7,000 to $10,000, senior (comparable to Amazon L6) $10,000 to $15,000, and leads $15,000+.
- Integration pod: a tech lead plus 2 senior engineers. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
- Platform pod: about 7 senior engineers, including a tech lead, an ML engineer and backend engineers, building a shared gateway and several integrations. About $75,000 to $105,000+ per month.
- Added engineers: 1 or 2 mid-level or senior engineers on your platform team at $7,000 to $15,000 each per month.
Total cost is team size × duration × monthly rate. You get a scoped plan, a price and the names of the people before work begins.
Dedicated team or staff augmentation?
A dedicated AI pod team fits when several systems need to change at once and one team should own the result. When your platform team owns the systems and needs AI experience alongside it, staff augmentation works well: hire AI engineers, Azure OpenAI engineers or AWS Bedrock engineers who work on your team and follow your change process.
When Ryz isn't the right fit
If the AI features built into your existing vendors (your CRM's or service desk's own assistant) already do the job, turn those on before building anything. If you want a proprietary integration platform to license, or engineers in European or Asian time zones, other providers fit better.
Related
FAQ
What does AI integration involve?
Connecting a model to the systems where work happens: pulling the right inputs, calling the model through a secure gateway, validating the output and writing it back to the right place, with monitoring and cost tracking around it.
Can you integrate AI with our existing CRM or ERP?
Yes, through their APIs and event streams, whether that is Salesforce, ServiceNow, Microsoft 365 or a custom internal system. We match engineers to the platforms you run, and they work within each system's API limits and permission model.
How much do AI integration services cost?
Ryz engineers typically cost $7,000 to $15,000 per engineer per month, with leads from $15,000. A three-person integration pod is about $35,000 to $45,000+ per month, and total cost is team size × duration × monthly rate.
How soon can integration work start?
After the scoping call we propose a team with names. Most of the timeline depends on scope and on how quickly your team grants sandbox access to the systems involved.
Can we keep data inside our cloud?
Yes. Teams commonly use AWS Bedrock or Azure OpenAI through private endpoints in your accounts, so requests stay on your network path and under your identity controls.
Questions we didn't answer? Email info@ryzlabs.com.