Product

The AI Pipeline That Turns Any Input Into Any Output

Define your target schema. Point Alpica at any source. Get clean, validated, standardised data with a full, hash-chained audit trail — deployed on your infrastructure, behind your firewall, on the LLM of your choice.

Capabilities

Every primitive the engine gives you.

No marketing diagrams — this is the reference. Every capability the schema, source, mapping, processing, and review layers expose.

Target

Schema editor

  • AI-generated parameter trees from JSON, schema, or free text
  • Hierarchical tree editor — types, units, mandatory flags, enum sets
  • Dual validation: readable text + executable Python, auto-synced
  • Add, remove, move, reorganise parameters in the tree
  • Versioning with diff tracking; draft persistence across sessions
Source

Source detection

  • Accepts CSV, JSON, TSV, and plain text
  • Client-side parsing with instant data preview before upload
  • AI-powered field detection and type inference
  • Auto-generated transformation functions with syntax highlighting
  • Sample data preserved for validation testing in later steps
Map

Mapping engine

  • Semantic-vector matching across source and target schemas
  • Confidence scores (0.0–1.0) on every proposed mapping
  • Three normaliser types: transformation, similarity, constant
  • Alternative candidate fields with comparative scores
  • Batch editor for constant mappings — $.path = value per line
  • Editable transformation logic with auto-sync between code and text
Process

Processing stages

  • Standardise — apply mappings, validate every value against the schema
  • Fix — TabPFN ML imputes missing or invalid values with confidence
  • Impute — extensible domain enrichment modules (e.g. CO₂ via PEFCR)
  • Per-value audit trail of source field, function, input, and output
Steward

Review interface

  • Per-entry review with full transformation provenance
  • Normalised + original + derivation + confidence + validation per field
  • Approve, reject, edit, or dismiss individual values
  • Batch operations across large datasets
  • Dismiss an AI transformation and re-run Fix
  • Quality scoring: completeness, accuracy, consistency (0.0–1.0)
Integrations

Connect it to anything.

Wire Alpica Data Clean into your existing stack — API, files, events, scripts. Built for engineers who want to keep their pipelines under control.

Available

REST API

FastAPI endpoints to upload data, run transformations, fetch results, and export. Authenticate, fire a request, get a structured response back.

curl -X POST /api/transform \
  -F file=@data.csv
FastAPIJSON
Available

File ingestion

Upload CSV, JSON, TSV, or plain text manually today. Cloud storage connectors (S3, GCS, Azure Blob) and REST polling are on the roadmap.

CSVJSONTSVS3GCS
Coming soon

Webhooks

Subscribe to platform events: transformation complete, validation failure, deploy. Signed payloads posted to your endpoint.

EventsSigned
Roadmap

CLI / SDK

Command-line interface and Python SDK for scripting batch operations, CI/CD pipelines, and unattended deployments.

PythonCLI
Audit & guarantees

Every value carries its provenance.

Every action on every artifact emits a structured event: actor, action, timestamp, hash of the data before and after. The log is replay-able, exportable, and cryptographically chained — you can prove that a given output came from a given input through a specific sequence of changes.

Quality is observable too: completeness, accuracy, and consistency are scored per dataset (0.0–1.0) and can be piped to Prometheus or Grafana alongside the rest of your platform metrics.

  • Immutable, hash-chained event log
  • Actor tracking: human, AI (LLM), system
  • Continuous quality scores exportable as metrics
  • Review → Approve → Deploy lifecycle on every artifact
audit.log·ref_a17f9c
live
10:23:14generatea1f4b2c8
11:09:02edit.materiala1f4b2c89d31e7af
11:45:08approve9d31e7afb8e271dc
11:48:33deployb8e271dcc3d14f02
11:48:34validatec3d14f02c3d14f02
5 events · chainedaihumansystem
Deployment

Your infrastructure, your model choice, no data egress.

A specs reference, not a marketing pitch. Everything runs inside your environment — bring the model, the storage, and the monitoring stack you already have.

Runtime
Containerised services, Kubernetes-ready
DockerHelm chart
Storage
Application + vector + cache, all under your control
PostgreSQLMongoDBQdrantRedis
LLM backend
Model-agnostic — bring your own provider or local model
OpenAIAnthropicGeminiLocal Llama
Ingestion
File upload today; cloud storage + REST connectors on the roadmap
CSVJSONTSVS3GCSAzure
Monitoring
Structured event log + quality scores exportable as metrics
PrometheusGrafana
Footprint
Indicative: 1 LLM-capable node, a handful of CPU services, vector DB
On-premVPCAir-gapped
Free proof of concept

Your source. Your schema. A real pipeline on real data.

Send us a representative sample of your data and the target schema. Our engineering team will build a working transformation pipeline on your own files — with the actual normaliser code, validation rules, and quality scores you'd see in production.

Your source files
Your target schema
Working pipeline, no obligation
Contact

See it on your own data.

Send us a sample and your target schema. We'll ship back a working transformation pipeline running on your real files — your source, your schema, your normalisers.

  • Talk to real engineers

    Every message is answered by someone who builds the product — not a sales funnel.

  • Fast, honest replies

    Expect a thoughtful response within one business day, with clear next steps.

  • Built around your context

    We adapt scope, security, and delivery to fit your team and your data.

GDPR-alignedAccepting pilots

Tell us about your project

We'll get back within one business day.

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