Data scattered across tools
Customer records, spreadsheets, documents, and app events live in separate systems with no dependable source of truth.
AI Solutions
AI-Ready Data Engineering
Turn fragmented business data into structured infrastructure for analytics, automation, and AI.
SofGent transforms raw, scattered, and messy business data into structured systems your AI products can actually use. We design schemas, pipelines, storage layers, and APIs so your data is ready for analytics, automation, and AI.
Outcomes
Structured for analytics and reportingReady for assistants and automationBuilt for production, not one-off cleanupIndustries
FintechHealthcareOperationsB2B SaaSThe Problem
Most teams want AI results but are still running on fragmented data.
No structure for AI
Messy schemas, missing metadata, and inconsistent fields make your data difficult to search, retrieve, or reason over with AI.
Manual reporting
Teams keep exporting CSVs, cleaning data by hand, and rebuilding the same dashboards every reporting cycle.
Poor decision making
Leadership ends up working from delayed or conflicting numbers, which slows execution and creates avoidable risk.
Our Approach
Build the data foundation before the AI layer becomes expensive guesswork.
Step 1
Data audit
We map your current sources, data quality issues, reporting gaps, and the business questions the system needs to support.
Step 2
Data structuring and schema design
We normalize entities, define schemas, add metadata, and shape the data model around analytics, automation, and AI use cases.
Step 3
Pipeline and storage setup
We implement ingestion, transformations, and storage across relational databases, warehouses, and vector infrastructure where needed.
Step 4
AI readiness layer
We add APIs, retrieval patterns, and documentation so your data can power assistants, automation flows, and AI products.
Deliverables
What ships at the end of the engagement.
Every engagement closes with a working production system, documentation, and a handover so your team owns it after we step out.
- Clean structured datasets with normalized entities and metadata
- Data pipelines for ingestion and transformation across files, apps, and databases
- API access layers for dashboards, internal tools, and downstream products
- AI-ready storage across relational and vector-friendly systems
- Schemas, data dictionaries, and implementation documentation
Use Cases
Where this service creates real leverage.
Analytics dashboards
Build reporting on top of a structured data backbone instead of fragile spreadsheet workflows.
Trusted reporting with less manual cleanup every cycle.
AI assistants
Give internal or customer-facing assistants access to clean, queryable business data.
Higher-quality answers and fewer hallucinations from bad context.
Automation systems
Trigger routing, decisions, and workflows from structured data instead of manual handoffs.
More throughput with less operational friction.
Reporting systems
Generate recurring reports faster, with less cleanup and fewer conflicting numbers.
Faster decisions based on a dependable source of truth.
Tech Stack
- PostgreSQL
- BigQuery
- Python
- FastAPI
- dbt
- Airbyte
- pgvector
- AWS
Why SofGent
Built for teams that need real systems, not demos.
AI-first data architecture
We do not stop at tidy databases. We shape the data foundation around how AI systems will actually consume and retrieve information.
Fast path from MVP to production
We build the first usable version quickly, then harden the architecture so it supports real growth instead of a temporary reporting patch.
Deep experience in document and structured data
SofGent brings hands-on delivery experience across document processing, knowledge systems, and structured operational data.
Focused implementation
We optimize for the shortest path to a reliable data foundation instead of dragging the project into months of overengineering.
Pricing
From $12,000
Audit + implementation sprint
Most data foundation engagements ship a first usable pipeline in 2–4 weeks.
FAQ
Answers to the questions clients ask before they book.
Don't see your question? Mention it on the strategy call — we'll cover the specifics for your stack and stage.
It covers the full foundation layer: data audit, schema design, ingestion and transformation pipelines, storage, metadata, and the API or retrieval layer that makes the data usable for dashboards, automation, and AI systems.
Yes. We regularly clean up and unify data from spreadsheets, CRMs, internal apps, document stores, SQL databases, and event streams. The point is to make your current data usable without forcing an unnecessary platform rewrite.
Usually both. The pipeline and schema work make the data dependable, and then we can expose that through dashboards, APIs, or downstream AI workflows depending on what the business needs first.
Most first-phase engagements land in 2–4 weeks. We scope the fastest usable version first, then expand from there if you need more sources, more automation, or a deeper AI retrieval layer.
Ready to start
Let's scope your ai-ready data engineering engagement.
Book a free 20-minute strategy call. We'll review your stack, surface the highest-ROI workflow, and outline a production path.