Tether messy data to the onerecord that's right.
Tether resolves messy free-text input to the canonical record in your authoritative data set. AI does the matching. Your team reviews the ambiguous ones. Your systems stay clean.
Everything you need to keep your reference data clean.
Semantic matching
AI finds the closest canonical record even when the input looks nothing like it on the surface.
Correction cache
Every human review becomes a cache entry. The same messy input never asks a reviewer twice.
Batch orchestration
Resolve a million rows with parallel embedding, vector search, and LLM calls — or stream matches one at a time.
Confidence tiers
Auto-accept above a threshold you set. Route ambiguous matches to your review queue. No-match stays no-match.
Reviewer workflow
A tight review queue with side-by-side diffs, keyboard shortcuts, and bulk actions. Built for the people who actually do the work.
Full audit trail
Every decision — cache hit, model verdict, human override — is logged with timestamps and actor.
Six deterministic steps from messy input to canonical record.
Each stage is observable, cacheable, and overridable. The fast path is cache; the slow path is a few hundred milliseconds end-to-end.
- 01
Normalize
Lowercase, strip punctuation, collapse whitespace. The input is rebuilt as a canonical search string before we look at it.
latency · <1ms - 02
Cache
Hit the correction cache first. Prior human decisions short-circuit the rest of the pipeline with zero model calls.
latency · 2ms - 03
Embed
Titan v2 generates a 1024-dimensional vector for the input. Cached entity embeddings are pre-computed.
latency · 18ms - 04
Vector search
Postgres pgvector returns the top-k nearest entities by cosine distance. Typically k=12.
latency · 12ms - 05
Disambiguate
Claude Sonnet scores the candidates against your field weights and domain glossary. Structured output, no parsing.
latency · 320ms - 06
Decide
Auto-accept above your threshold, route to review if ambiguous, emit no_match if nothing clears the floor.
latency · <1ms
Built for the messiest reference data in your stack.
Migrate clean, not dirty.
Cutting over to a new ERP? Tether resolves every legacy SKU, vendor, and GL code to the target system's canonical set before you hit the switch. Zero rework, zero duplicated masters.
One master, one source of truth.
Mergers and acquisitions double your vendor list overnight. Tether consolidates aliases, spelling variants, and legal-entity mismatches into a single master list — with an auditable decision trail.
Every row tagged, every night.
Nightly ETL for 500k+ transactions a day. Tether batches the whole job, caches every human correction, and ships a confidence-scored match back to your warehouse before the ELT sync finishes.
Real numbers from production pipelines.
Simple pricing. No per-seat surprises.
Pay for matches, not headcount. Every plan includes unlimited reviewers, the correction cache, and a full audit trail.
Starter
Try Tether on a single entity set with a small team.
- 1 entity set
- 10,000 matches / month
- Up to 3 reviewers
- Correction cache
- Community support
Team
For data teams running production pipelines.
- Unlimited entity sets
- 500,000 matches / month
- Unlimited reviewers
- Domain glossaries
- Field-weight tuning
- Audit log export
- Priority email support
Enterprise
For regulated industries and self-hosted deployments.
- Everything in Team
- On-prem / VPC deployment
- Bring your own Bedrock account
- SSO + SCIM
- Custom retention policies
- 99.95% uptime SLA
- Dedicated support
All plans bill in USD · cancel anytime · volume discounts available
Tether your data to a single source of truth.
Start with a single entity set on the free tier. Wire up your first pipeline in an afternoon. Scale when you're ready.