Automation and Integrations
Data models that stay fast and correct as you grow
Your database outlives every screen and feature built on top of it. We design data models that stay correct, fast and easy to change for years.
The problem
The problem this solves
Most software problems that are expensive to fix start in the Database. Tables designed in a hurry for the first feature do not fit the tenth. The same information is stored in several places and slowly drifts apart. Missing constraints let bad data in. Queries that were instant with a thousand rows take seconds with a million.
These problems are hard to see from the outside. The product works, but every new feature takes longer, reports disagree, and developers become nervous about changing anything. Backups exist but nobody has tested a restore.
A good data model makes the rest of the software simpler. Each fact is stored once. Rules such as "every invoice belongs to a customer" are enforced by the database itself. Indexes match the questions the application asks. Migrations are scripted and reversible. Access is limited to what each part of the system needs. For products with many customers, the model supports multi-tenant separation from the start.
We work mostly with PostgreSQL and MySQL, and with SQLite or specialized stores where they fit. In our deal aggregator case study, a database trigger enriched every new product automatically, which kept the application code simple and the data consistent across dozens of sources.
We also help with existing databases: finding slow queries, adding the right indexes, cleaning up duplicated data and planning a safe path to a better structure without stopping the business.
Good design is documented. You get a diagram of the data model and a short explanation of the important rules, so every new developer understands the data before they change it.
What you get
What you get
-
A data model
Tables, relationships and constraints designed around your business rules.
-
Enforced data rules
Constraints and checks that stop bad data at the door.
-
Query performance
Indexes and query rewrites for the questions your application asks most.
-
Safe migrations
Scripted, reviewed and reversible changes to the structure.
-
Backups and restores
Automatic backups and a tested restore process.
-
Access control
Separate users and permissions for applications, reports and people.
-
Archiving and retention
Old data archived or removed according to your rules.
-
Documentation
A diagram and plain explanation of the data model.
How we build it
How we build it
-
1
Understand the domain
We learn the business entities, rules and the questions you need answered.
-
2
Model
A data model drafted, reviewed and tested against real scenarios.
-
3
Plan performance
Indexes and structure planned for expected data volumes.
-
4
Implement
Schema, migrations, seed data and tests created.
-
5
Operate
Backups, monitoring and access rules in place before launch.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
-
Drafting a data model from requirements and sample records.
-
Analyzing slow query logs and suggesting indexes.
-
Writing migration scripts and tests.
-
Finding duplicate or inconsistent records.
-
Generating diagrams and documentation.
Where people decide
-
How business rules become data rules.
-
Trade-offs between flexibility and strictness.
-
Which data must be kept, archived or deleted.
-
Who can access sensitive data.
-
When a migration is safe to run in production.
Is this right for you?
When this is the right choice
A good fit when
-
You are starting a new product and want the foundation right.
-
Queries and reports have become slow.
-
Data is duplicated or inconsistent across your system.
Consider something else when
-
All your data lives inside a SaaS tool you cannot change. Improve the reporting instead.
-
The data is small and simple, such as a short list in a spreadsheet.
Timeline and cost
What affects the timeline and cost
Designing the data model for a new product is usually part of the first few weeks of a project. Performance work on an existing database often shows results within days; restructuring a large database is planned in careful stages.
We do not publish fixed prices because scope drives cost. How we estimate.
-
Domain complexity
More entities and rules take longer to model well.
-
Data volume
Large tables need careful indexing and migration planning.
-
Existing structure
Changing a live database is slower than designing a new one.
-
Uptime needs
Migrations without downtime need extra planning.
-
Security needs
Sensitive data needs encryption and stricter access.
-
Connected systems
Other systems reading the database must keep working.
Keep exploring
Related services, solutions and reading
Related services
View all related services- Automation and Integrations CRM, payment and API integrations, MCP connections and workflows that stop people copying data by hand.
- Admin panels The back-office screens your team uses to manage users, content, orders and settings without a developer.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Dashboards and reporting Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
- App backend and APIs The servers, databases and APIs behind your app: accounts, data sync, notifications and payments.
- Data migration Move data from an old system to a new one, cleaned, checked and without losing a record.
Solutions
View all solutions- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
- Price monitoring Collect competitor prices automatically, match them to your products and alert you when something changes.
- Invoice processing Read supplier invoices, match them to orders and push approved ones into accounting, with exceptions flagged for review.
Industries
View all industries- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
- Logistics and transportation Driver apps, order and shipment tracking, document processing and integrations for logistics and delivery companies.
- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- Property management Tenant portals, maintenance tracking, owner statements and inbox automation for property managers.
- Ecommerce Online stores, product data cleanup, support assistants, price monitoring and order automation for online retailers.
- Retail Stock forecasting, sales dashboards, feedback analysis and store tools for retailers with shops and online sales.
Case studies
View all case studies- Fashion deal aggregator Sale items from more than thirty brands are collected every night and classified consistently by a three-stage pipeline.
- Payroll system for an IT services company Payroll is calculated from recorded inputs, reviewed once and sent as payslips in a single batch, with every change logged.
- Beach rental booking system A long-running booking system kept earning while it was extended, tested and given a safe, gradual path to a modern stack.
- Facility management platform Every request, inspection and cost for every building now lives in one place, with a clear owner and status.
- Goal and effort tracking app A product design built around handwritten daily proof, locked plans and honest self-scoring. The build is in progress.
- Tax filing service platform Clients upload documents and see where their case stands, and staff work from one queue with deadlines and reminders.
Guides and articles
View all guides and articles- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
- How to set up automated reporting Replace manual weekly and monthly reports with dashboards and summaries that update themselves.
- Technology stack The frameworks, databases, AI tools and hosting ExecMedia uses to build websites, SaaS products, mobile apps and AI features, and how we choose them.
AI models
View all ai models- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Cohere Embed v4 Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Voyage 4 Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
MCP servers
View all mcp servers- Databases Servers that let AI query and, where allowed, change data in SQL, NoSQL, analytics and vector databases.
- BigQuery Google's remote BigQuery MCP server lets AI list datasets and tables and run SQL, with a read-only query tool and IAM controls.
- Chroma Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
- Elasticsearch Elastic's Agent Builder MCP endpoint lets AI search indices, run ES|QL queries and read mappings and documents in Elasticsearch.
- MongoDB MongoDB's official MCP server lets AI query collections, run aggregations and manage Atlas clusters, with a read-only flag for safe use.
- MySQL A community MCP server that lets AI run SQL against MySQL, read-only by default, with insert, update and delete each switched on separately.
Glossary terms
View all glossary terms- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- Backend The backend is the part of an application that runs on servers, storing data, applying business rules and serving the frontend through APIs.
- Multi-tenant Multi-tenant describes software where one application and database serve many customers, called tenants, while keeping each customer's data separate.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
FAQ
Questions about database design
Have a question that is not here? Ask us directly.
Both are excellent. PostgreSQL has richer features for complex data and is our usual default for new products; MySQL is a fine choice, especially where a team already knows it. See PostgreSQL vs MySQL.
Usually yes. Most slowness comes from a few queries missing indexes or doing too much work. We measure first and fix the biggest problems. See product scaling.
It can, through a read-only connection with strict limits. We usually recommend a controlled layer or MCP server. See connecting AI to your database safely.
Yes. Reporting often benefits from a separate, simplified layer. See dashboards and reporting.