Architecture first. Pipes second.
Most Salesforce integration work fails not because the technology is hard, but because nobody owns the architecture. We design, build, and run the integration and data layer so Salesforce and the rest of your stack work as one.
Designed and run by the same engineers.
Three things that set our Data & Integrations practice apart.
“Integrations look like plumbing until the day a vendor changes their API contract and three downstream systems go down at once. We design for that day before it shows up.”
Salesforce ↔ everything else is most of what we do
We own the Salesforce-to-rest-of-stack work most consultancies fumble. ERP, finance, product telemetry, support tools, identity, industry-specific stacks. Ten years of getting that boundary right. See Custom Solution Development for the wider platform work that often lives alongside.
One team, every cloud
The hyperscalers under the same team. We pick the right tool for the workload. No procurement gymnastics.
We run it in production
We design the architecture, build it, ship it, and operate it. SLA-backed sustaining with defined response times, observability baked in. Architecture decisions hold up because we're the ones living with them. See it run in the Skeens Salesforce integration case study, where an owned integration replaced roughly $40K a year in middleware.
Multi-cloud, by default.
Salesforce is the application and agent layer. The hyperscalers are the infrastructure underneath. We work across all of them.
A multi-tenant application platform with Core, Data 360 (formerly Data Cloud), Agentforce, MuleSoft, Tableau, Slack, Commerce, and the Industry Clouds. The strategic layer: system of record, system of engagement, and system of intelligence.
Lambda, Step Functions, S3, Redshift, Glue, EventBridge, and the full serverless data stack.
Azure Functions, Data Factory, Synapse, Event Hubs, Cosmos DB, and Power Platform connectors.
BigQuery, Dataflow, Pub/Sub, Cloud Functions, Vertex AI, and Looker analytics.
What Data 360 is, and isn’t.
Salesforce Data 360, formerly Data Cloud, aggregates sources, resolves identity, and activates unified profiles. Most failed implementations expected it to be something else.
Not your MDM, ETL, or warehouse
It unifies data and resolves identity for segmentation and activation. It can enhance an MDM, but it isn't built to master a golden record and push it back to source systems, transforms aren't a place to fix data quality, and bulk export isn't the design: zero-copy sharing, activation, and the APIs are. The questions to ask before you implement are where every engagement starts.
The week-one decisions that last for years
The model comes first: a data dictionary, the system of record per domain, and match rules chosen by consequence, deciding first what a wrong merge of two people would cost. Then the org shape: provisioning on your home org gives the richest CRM integration, a dedicated org is more flexible, and region availability can force the choice for you. The Dreamforce developer session shows the working version: one Unified Individual ID, queried from Apex and LWC.
Grounded into Agentforce
Salesforce positions Data 360 as the live context Agentforce runs on, and identity resolution decides whether the agent's answers can be trusted at all. The agent work lives on Agentforce. For ISVs putting Data 360 in a product, 2GP packaging has been possible since 2024; build, buy, or buy-to-build is the scoping question, and the listing runs through Product Development.
“High level of technical expertise and understanding for complex business cases. The partner showed a great way to guide [us] through the technical landscape and help us find the right direction for our data360 solution to take.”
Verified project review · June 2026 · Read it on our AgentExchange listing
Where most integration projects stall. Unless someone owns the arc.
We connect Salesforce to ERP, finance, product telemetry, support, identity, and industry stacks. Five stages from discovery to sustain. We run the whole arc or join wherever you need us.
Discover
Stakeholder interviews, system inventory, data flow mapping, integration audit. What flows where, what's broken, what's missing.
- Stakeholder interviews and source-of-truth mapping
- System inventory across Salesforce, ERP, finance, support, telemetry
- Flow-by-flow integration audit
- Data quality and readiness assessment
- Quick-win identification
Architect
Domain models, service boundaries, event topology, identity, security. The shape settled before anyone picks an iPaaS.
- Domain modeling and canonical schemas
- Event topology and sync patterns (real-time, batch, CDC)
- Identity, access, and security models
- iPaaS / ESB / native-API selection
- Reference architecture and decision records
Build
Pipelines, connectors, event-driven backbones. AI-augmented engineering with automated tests on the flows the business depends on.
- Pipeline and connector implementation
- AI-augmented agile delivery
- Automated test coverage on critical flows
- Observability and error tracking baked in
- Contract testing across system boundaries
Cutover
Data migration, customer-zero rehearsals, rollback paths, change management. Sequenced so the business keeps shipping.
- Data migration and backfill
- Cutover rehearsals and dry runs
- Rollback paths and reconciliation
- Change management and user training
- Phased rollout across regions or teams
Sustain
SLA-backed sustaining, observability, incident response. Pipelines stay healthy through every Salesforce release and every system upgrade.
- SLA-backed Tier 3 sustaining
- Release-cycle regression coverage
- On-call rotations and incident response
- Performance, cost, and throughput optimization
- Clean handoff to your team when it's time
Six capability areas. Same depth in each.
The data and integration layer covers more than the Salesforce boundary: architecture, engineering, AI, protection, continuity, and the Salesforce connections everything else depends on.
Integration architecture
The patterns that keep systems talking to each other when something upstream changes.
RESTful and GraphQL API design, versioning strategy, contract testing, and documentation.
Pub/sub, event sourcing, CQRS patterns, and asynchronous processing architectures.
MuleSoft, Workato, Boomi, and legacy ESB modernization to cloud-native patterns.
API-led connectivity layers, shared libraries, schema registries, and integration templates.
Data engineering & analytics
We turn raw data into something your business can act on.
Kafka, Kinesis, Pub/Sub, and Change Data Capture for real-time data movement and enrichment.
Delta Lake, Iceberg, and unified storage layers that combine warehouse performance with lake flexibility.
Snowflake, Redshift, BigQuery, and Synapse: schema design, ETL/ELT, and query optimization.
Metrics layers, reverse ETL, data products, and activation pipelines to operational systems.
AI & agent infrastructure
What keeps AI agents reliable in production. Customer-facing agents shipped as products live on Product Development; internal agents pointed at your own team live on Custom Solution Development.
Embedding pipelines, vector stores (Pinecone, Weaviate, pgvector), and chunking strategies.
LLM evaluation harnesses, drift detection, cost tracking, and prompt versioning.
Multi-model routing, fallback chains, rate limiting, and provider abstraction layers.
Tool-use patterns, function calling schemas, sandboxed execution, and human-in-the-loop triggers.
Data protection
Data your teams can use because they trust how it's protected. Application-level controls and security review prep live on Application Security.
Automated data classification, lineage mapping, impact analysis, and data catalog management.
At-rest and in-transit encryption, KMS integration, key rotation, and secrets management.
Role-based access, attribute-based policies, data masking, and audit trail implementation.
GDPR, CCPA, HIPAA data handling, consent management, and right-to-deletion workflows.
Business continuity & DR
When things go wrong, recovery should be a process, not a panic.
Automated backup schedules, point-in-time recovery, cross-region replication, and validation testing.
Active-passive, active-active, and pilot light patterns across cloud regions and providers.
Documented recovery procedures, tabletop exercises, and scheduled failover drills.
Health checks, automated failover triggers, DNS switching, and recovery time validation.
Salesforce ↔ everything else
The integration patterns that connect Salesforce to ERP, product telemetry, support tools, and industry-specific systems.
NetSuite, SAP, Oracle: order sync, invoice reconciliation, and master data harmonization.
Usage data from your product into Salesforce for health scoring, expansion signals, and churn prediction.
Jira, Slack, Zendesk, Confluence: bidirectional sync for case escalation and team coordination.
Industry-specific systems: hospitality (PMS, CRS, POS), restaurant tech, financial services. Plug into the operational stack your customers run on.
Common questions
What data and integration services does Aquiva provide?
Multi-cloud data engineering and integration architecture across Salesforce, adjacent platforms, and hyperscalers. This includes API design, event-driven architecture, iPaaS orchestration, streaming, analytics, data protection, and business continuity.
Can Aquiva integrate Salesforce with other cloud platforms?
Yes. Salesforce-to-everything integration is a core strength. That includes ERP, data warehouses, marketing platforms, and custom backends on the hyperscalers.
Does Aquiva handle data migration and business continuity?
Yes. We design and execute data migrations, build backup and recovery strategies, and implement business continuity plans to protect your integration layer.
Does Aquiva do Salesforce Data Cloud implementation?
Yes. Data Cloud is now Salesforce Data 360, and we implement it as what it is: data-model design, identity resolution, calculated insights, segmentation, and activation, tied to Agentforce grounding and to ISV products on the AgentExchange. The engagement starts with the question set before the first source is connected.
What is Data 360 vs Data Cloud?
Data 360 is the current name for Salesforce Data Cloud; Salesforce renamed it as part of the Agentforce 360 rebrand. Same platform: ingest, unify, resolve identity, segment, activate. If you searched for a Data Cloud partner, this is that work.
Is Aquiva a Salesforce integration consultant or a build partner?
Both. The team that architects your Salesforce integration is the same team that builds it and keeps it running, so the design has to survive contact with production. You get the consulting and the engineering from one partner accountable for the whole boundary.
Your systems are only as good as the layer between them.
One team from architecture through build and into operations.