Data Infrastructure Engineering for SaaS & Software Companies
Your database was fine at 10,000 users. At 200,000, it slows down in ways nobody predicted, because growth changes which queries are slow and which caches stop working. We build the database, real-time, AI, and platform infrastructure underneath your product to hold at the scale you're growing into.
Where SaaS Data Infrastructure Actually Breaks
A query plan that returns in 50ms at 10,000 rows doesn't degrade linearly, it falls off a cliff once a table crosses an index threshold or a join stops fitting in memory. Most teams bolt on caching to mask the symptom instead of fixing the query or the schema underneath it.
The same pattern shows up in AI features. A retrieval pipeline built as a weekend script works fine in a demo and then returns the wrong user's data in production because nobody added row-level access control. And cloud database spend creeps up quietly because nobody owns the line item until finance asks why it doubled.
None of this is a reason to rebuild the platform from scratch. It's a reason to fix the specific query, pipeline, or access rule that's actually causing the pain, in the order that returns the most, and to build the next feature on infrastructure that won't need the same fix again in six months.
We scope database performance, real-time infrastructure, and AI data infrastructure to the specific queries, pipelines, and features actually limiting your product, not a generic platform rebuild.
Five Problems We See Repeatedly, and How We Handle Them
Real scenarios, not a checklist of generic capabilities.
A dashboard query that returned in 200ms during the demo takes four seconds once real customers start filling the table.
We profile the actual query plan, add the missing index or fix the N+1 pattern, and tune connection pooling so a spike in concurrent users doesn't queue behind a handful of expensive queries.
Database EngineeringYour usage-based billing meter runs on an hourly cron job, so a customer's invoice doesn't reflect what they did an hour ago, and support gets the ticket.
We build CDC pipelines off your transactional database so usage events reach billing and analytics in near real time, without adding polling load on production.
Real-Time Data EngineeringAn engineer shipped an AI feature as a script that queries every document on every request, and a staging bug report already shows one tenant's data in another tenant's answer.
We build retrieval as governed infrastructure — indexed vector storage, row-level access tied to your existing roles, and agent memory stored as queryable rows instead of a shared key.
AI Data InfrastructureEvery new dashboard or export request turns into another Slack message to the one engineer who actually understands the data pipeline.
We build a self-service platform with golden-path pipeline deployment and data contracts, so product and analytics teams stop waiting on one person's calendar.
Data EngineeringChurn gets flagged in a QBR after an account has already gone quiet, because nothing was scoring the risk while it was still recoverable.
We build a churn prediction model off your product usage data, so customer success can act on accounts actually trending toward cancellation, before the renewal conversation.
Data AnalyticsTechnology We Work In
PostgreSQL for the transactional core, Redis for caching, Kafka for event streaming, Kubernetes for deployment, and Next.js/GraphQL for the product layer built on top.
We work hands-on with: PostgreSQL, Redis, Kafka, Kubernetes, Next.js, GraphQL.
What's Included, By Category
Database & Performance Engineering
- Query optimization and indexing for PostgreSQL, MySQL, or Aurora
- Connection pooling and resource tuning under concurrent load
- Replication, failover, and backup/recovery design
- Cost right-sizing tied to actual utilization, not the original provisioning guess
Real-Time & AI Infrastructure
- CDC and event-driven pipelines on Kafka
- Vector and knowledge-graph storage for retrieval features
- MCP servers and agent memory for AI product features
- Row-level access control for multi-tenant and AI-facing data
Platform & Integration Engineering
- Self-service pipeline deployment and data contracts
- Custom API development and third-party integrations (Stripe, Salesforce, HubSpot)
- Legacy application modernization for acquired or inherited codebases
- Cloud data warehouse cost optimization (Snowflake, BigQuery)
Market Segments Served
We work with SaaS and software companies at every stage, from early growth to post-acquisition consolidation.
- Seed-to-Series B products whose database was sized for the MVP, not the current customer count
- Usage-based or metered-billing products where invoice accuracy depends on real-time event data
- Vertical SaaS handling regulated customer data inside a broader compliance program
- Products adding an AI or chat feature on top of an existing PostgreSQL database
- Engineering teams without a dedicated DBA or data platform team
- Later-stage companies consolidating acquired products onto one data platform
Delivery Lifecycle
Discovery & Assessment
We review your slowest queries, cloud and warehouse spend, and how AI or real-time features are currently built, and identify which gaps are actually limiting growth.
Architecture & Design
We design the fix for your highest-cost gaps first, with an explicit call on what needs real-time or dedicated AI infrastructure versus what doesn't.
Build & Integration
We implement the database, streaming, or AI infrastructure changes end to end, integrated with the systems your team already runs.
Testing & Validation
We validate the fix against real production data and real load, not a synthetic fixture, before calling it done.
Launch & Ongoing Coverage
We document runbooks, train your team to run what we built, and stay on retainer for incidents and the next scaling milestone.
Why Teams Work With Us
Depth Across the Whole Stack, Not Just One Layer
A DBA shop tunes queries. An AI agency builds RAG pipelines. A platform consultancy designs pipelines. We bring database, real-time, AI, and platform engineering as one team, so a problem that crosses two systems doesn't fall into the gap between two vendors.
A Team, Not One Person's Calendar
A single in-house hire means business hours and one person's availability, with everything waiting when they're out. We bring a team behind every engagement, so an incident doesn't wait on someone's vacation.
Scoped to the Actual Problem, Not a Fixed Package
Every engagement starts with a diagnostic against your real queries, pipelines, and spend. You get a prioritized list of what's actually wrong, and pricing scoped to that, not a generic package sized for someone else's data.
No Dependency by Design
Runbooks and documentation are part of the deliverable, not held back to keep you on retainer. If ongoing coverage is still the right call for what you're running, we'll tell you why, not just assume it.
Related Services
Frequently Asked Questions
Find Out Where Your Product's Data Layer Actually Breaks
We schedule a call to hear what's going on, then a second call to review your database, pipelines, and AI features and tell you directly which gaps carry real risk.