The real cost of building
entity resolution in-house
Matching customer data yourself means maintaining fuzzy-matching rules, a real-time search stack and drift monitoring — and still not knowing if accuracy is improving. Tilores unifies it all behind one API, with matching you can tune and measure.
- Fuzzy-match threshold drift · source B URGENT
- Re-index blocking keys after schema change BLOCKED
- Golden-record merge conflicts WORKING
- False-positive dedup review WEEKLY
- Reconcile customer schema across 6 sources MONTHLY
Used by AI teams, risk teams, and data engineers at MediaPrint, Inato, Grover, and Cofinity-X
Build vs Buy: every stage you'd own
Building entity resolution means owning all twenty stages below. Flip Tilores on and watch what's actually yours to run.
// how long does "build" really take?
That's what it took to build the first version of Tilores — with a team that had already built a consumer credit bureau. The matching logic is the easy part; the years go into everything around it.
Continuous resolution, zero heavy lifting
One endpoint, one data model, and matching you can actually reason about.
One API, the whole pipeline
Ingestion, normalization, matching, clustering, golden records and real-time search — through a single endpoint. No pipeline to build or run.
Matching you can tune
Adjust rules and field weights to fit your data — without rebuilding a matching engine or retraining a model from scratch.
Real-time at scale
Sub-150ms lookups across millions of records. Proven at 110M+ records resolved into 60M entity clusters.
Deploy in your own cloud
Run on AWS or on-premise, SOC 2-compliant and GDPR-ready — your data stays in your environment.
Matching real-world identities is harder than it looks
Customer data arrives in every format imaginable. Even wiring up matching libraries yourself, accuracy stays inconsistent — and without monitoring, you can't tell which setup performs best. Here's what teams underestimate.
Messy, inconsistent data
Typos, abbreviations, missing fields and free-text formatting make naive matching fail on the first real dataset.
The same person, many ways
"Bob" and "Robert", maiden names, transliterations, swapped name orders — all the same entity, none of them equal.
Cross-system variants
CRM, billing, support and product each model a customer differently. Reconciling them is a project on its own.
Scale breaks naive matching
Comparing every record to every other is O(n²). Blocking and indexing become their own engineering effort.
Real-time expectations
Product and support need sub-150ms answers, not an overnight batch job — that raises the infrastructure bar sharply.
Accuracy you can't see
Without evaluation tooling there's no reliable way to know whether a rule change made matching better or worse.
Constant drift
New sources, new formats and changing data mean hand-tuned rules rot — accuracy quietly degrades over time.
The compliance burden
SOC 2, GDPR and data-residency for a system holding all your customer data land squarely on your team.
These are the same problems Tilores already solves — without you maintaining matching rules or manually tracking accuracy.
Why teams try to build — and what they learn too late
Most builds start with good reasons: control, customization, and perceived savings. Then they turn into fragmented pipelines, unpredictable accuracy, and hundreds of engineering hours. Tap a card for the reality.
Skip the rebuild. See it on your data.
Book a demo and we'll resolve a sample of your own records live — so you can see exactly how Tilores matches, deduplicates and returns golden records with accuracy you can measure.
Build vs Buy: what's really at stake
Ten dimensions, two paths, the same goal.
A seasoned team of six took ~3 years to build the first version of Tilores.
Live in under 24 hours with instant API access.
Depends on in-house expertise; hard to measure or trust.
Configurable rules with accuracy you can measure and prove.
Ongoing monitoring, re-tuning and QA to prevent silent drift.
Fully managed — we handle monitoring, tuning and ops, so you don't.
Complex DevOps and constant re-indexing as volume grows.
Proven across 110M+ records — scales automatically.
Sub-second search at scale needs serious infrastructure.
Sub-150ms lookups by any attribute, at scale.
Hand-tuned rules that are risky to change and easy to break.
Adjust and roll back matching logic safely.
Difficult to benchmark or detect a regression.
Evaluation and tracking built in — measure gains over time.
Your team debugs matching issues alone.
Our team monitors, resolves and optimizes with you.
Regular audits, documentation and certification on you.
SOC 2-compliant and GDPR-ready by default.
Unpredictable, rising with infra, maintenance and staffing.
Transparent, usage-based pricing per resolved record.
Building in-house can make sense for highly specialized or IP-sensitive systems. Everyone else loses time maintaining matching rules, debugging edge cases, and guessing whether accuracy is improving. Tilores gives you a unified system that resolves entities in real time — and proves it with metrics.
Focus on your product, not the pipeline.
Start resolving scattered customer data in real time — with matching you can tune and accuracy you can measure — without rebuilding it from scratch.