datafate
Selected work

Proof, in production.

A sample of AI systems we've designed, shipped, and operated — voice agents, RAG assistants, migrations, search, and custom models. Client names are withheld under active NDAs; sectors, stacks, and outcomes are real. References available on request.

01Logistics · Series B
2025 · 2 engineers · 9 weeks

Shipped a voice agent that now handles 40% of support calls end-to-end.

Support was drowning in the same five calls: where is my order, reschedule it, refund it. We built a voice agent with tool access to their live order system, human handoff for anything delicate, and an eval suite that scores every release on resolution quality before it touches a customer.

Our contribution

  • →Voice pipeline (STT / TTS)
  • →Tool-use orchestration on order data
  • →Human handoff & sentiment routing
  • →Per-release eval suite

Python · FastAPI · WebRTC · Whisper · LLM orchestration

Results

40%Calls resolved by agent
−70%Support wait time
<1sAgent response latency
02Healthcare · Growth
2024 · 2 engineers · 8 weeks

Shipped a clinical RAG assistant that providers actually trust.

Clinicians needed answers grounded in their own protocols, not a generic chatbot. We built a retrieval and evaluation harness with citations, guardrails, and an offline eval suite so every release could be measured before it reached a patient-facing surface.

Our contribution

  • →Retrieval & eval harness
  • →Citation + guardrail layer
  • →Human-review workflow
  • →Cost & latency controls

Python · pgvector · LLM eval · FastAPI · Next.js

Results

94%Grounding, eval set
100%Answers cited
0Ungrounded answers served
03Fintech · Series A
2025 · 3 engineers · 12 weeks

Migrated manual reconciliation to an AI-native pipeline — 100% automated.

Reconciliation lived in spreadsheets and it was starting to hurt. We built an ingestion and extraction pipeline over their ledgers and partner statements, layered an LLM matching step with human review for the edges, and rolled it out phase by phase until the spreadsheets were gone.

Our contribution

  • →Data ingestion & extraction pipeline
  • →LLM matching layer
  • →Phased migration from spreadsheets
  • →Human-review workflow for edge cases

TypeScript · PostgreSQL · Temporal · LLM eval · Terraform

Results

100%Automated reconciliation
−9 hrsManual close, per day
0Spreadsheets left
04E-commerce · Growth
2025 · 2 engineers · 8 weeks

Replaced keyword search with AI search across a 1M+ product catalog.

Keyword search failed the way customers actually ask. We built hybrid retrieval — vectors plus keyword plus reranking — with an indexing pipeline over the full catalog and answers that cite the products they came from, then tuned relevance against a held-out eval set.

Our contribution

  • →Hybrid retrieval (vector + BM25)
  • →Catalog indexing pipeline
  • →Reranking & relevance tuning
  • →Cited answers + analytics

Python · pgvector · FastAPI · Next.js · Redis

Results

+23%Search conversion
94%Relevance, eval set
<80msp95 query latency
Domain expertise

Where we go deep.

Depth from real production environments — integrations we've built, scaled, and operated under pressure.

Voice & telephony

Call handling, IVR, SIP integration

E-commerce & retail

Catalog search, recommendations, support

Fintech & payments

Reconciliation, compliance, ledgers

Digital health

Clinical retrieval, compliance, EHR

Logistics

Dispatch, tracking, support automation

Legaltech

Contract analysis, document pipelines

Developer tools

SDKs, APIs, developer experience

SaaS & internal ops

Copilots, knowledge search, automation

[ Your system could be next ]

Let's build the
proof together.

If you're considering AI for your product — agents, search, or migration — we should talk. We'll tell you honestly whether we're the right team for it.

business@datafate.io
Response under 24 hours AI integration for production systems.