What Does DIY Look Like?

A typical in-house build follows a familiar path: copy operational data into a cloud warehouse such as Snowflake, write a semantic layer or prompt context describing tables and metrics, wire an LLM to generate SQL, and expose it through a chat app or MCP server. Meanwhile, the questions it cannot answer still go to analysts as report requests. Every step is feasible. The cost is in doing all of them, and keeping them working.

The Hidden Effort: Building the Context

An LLM does not know that “active member” excludes charged-off accounts, which of three date columns is the posting date, or how six tables join to produce a loan balance. Someone has to write all of it down — and rewrite it as schemas change. In a DIY approach, that someone is your data team.

What Your Data Team Has to Build

• Schema documentation. Descriptions for every relevant table and column, including legacy names and codes.
• Join paths and grain. Which tables relate, how, and at what level of detail — the most common source of wrong answers.
• Metrics and business terms. Definitions for KPIs, aliases, and synonyms (“top accounts,” “deposits,” “NPLs”).
• Value grounding. Lookups so the model matches “Main St branch” to the right code, instead of hallucinating.
• Example queries and evals. Curated question-SQL pairs and a test set to catch regressions every time the model or schema changes.
• Governance plumbing. Row- and column-level security, Entra ID integration, audit logging, PII handling, and an MCP server for agents.

Cost and Savings Analysis

Tursio Savings

$499,250 per year — 66.6% lower total cost, for unlimited users at 25,000 queries per year.

Why DIY Costs More Than It Looks

• Moving data is a project in itself. Migrating to a cloud warehouse adds ETL pipelines, compute spend, and a second copy of sensitive data — and regulated institutions often cannot move core data at all.
• Context decays. Every schema change, new product, or reorganized report breaks hand-written context. Maintenance never ends, and the people who wrote it move on.
• One-shot SQL is fragile. Simple text-to-SQL works in demos and fails on multi-table, multi-step business questions. Getting to production accuracy takes far longer than the prototype.
• Tokens add up. Without compact context and grounding, prompts carry large schema dumps. DIY setups typically spend about 7x more per query on tokens ($0.20 vs. $0.03).
• Opportunity cost. Your best data engineers spend a year on plumbing instead of the analyses and products only they can build.

How Tursio Is Different

• Context built automatically. Tursio infers the context graph from schema, query history, and BI definitions on day one, then tunes it from user feedback. Your team reviews and refines; it does not author from scratch.
• No migration. Tursio connects to 50+ sources in place — SQL Server, Oracle, Symitar, Snowflake, Salesforce, NoSQL — so there is no warehouse to stand up.
• Production-grade from the start. Operator-tree planning, value grounding, RBAC, audit, Entra ID, and MCP — packaged in one on-prem Docker image.
• Keep control. Bring your own LLM keys and keep everything inside your perimeter, so the “privacy” and “control” reasons to build are already met.
• Build on it. For teams that want to build, Tursio's APIs (search, context tuning, debugging) provide the hard foundation, so engineers can focus on the experience on top.

Side-by-Side Comparison

Bottom Line

The model is the easy part. Context, grounding, governance, and maintenance are what turn a demo into a system people trust — and they are what make DIY expensive. Tursio delivers them packaged and on-prem, cutting total cost by about two-thirds while keeping your data and your models under your control.