Computer Vision Development
A Computer Vision Development Company That Is Built On Consent
Computer vision is getting software to read images and video reliably — detecting objects, reading text, checking quality and, where consented, recognising a face. A face template is biometric, not a password anybody can reissue, so most judgement sits in what you store, where matching runs, and how deletion works.
Triple Protection Guarantee
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Triple Protection Guarantee:
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Years In Business
0
+
Engineers On Staff
0
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Avg. Engineer Exp.
0
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Where Face ID Fails
Consent First
Then Enrol
Senior Engineers
Vetted Only
Timezone Overlap
Live Hours
Templates Only
No Raw Photos
Delaware LLC
US Entity
On-Device Path
Where It Fits
Bias Test Suite
Per Cohort
Retention And Delete
Designed In
Human Review
On Matches
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Biometric Owner
You
Trusted By Startups
What Our Computer Vision Company Actually Does
A computer vision development company earns its fee in the decisions taken before any model is chosen. Whether you store an image or a template. Whether matching happens on the device or on your servers. For a face, who consented, to what, and how they withdraw it. How long a record lives and what a deletion request removes. Get those wrong and model quality will not save the product, because what you are holding is an identifier nobody can reissue.
Stallyons is registered in Delaware as a US company, and our engineers work a time-zone window agreed before the project starts, with four-plus hours of daily overlap. You sign a US contract, run diligence on a US entity and pay one US invoice. Behind the work sits twelve-plus years of delivery across six continents and around thirty-five engineers, averaging four-plus years of production experience. Teams come to us when a face feature has to satisfy a security review and a privacy review, not only a product demo.
What A Face ID Build Covers
Consent designed in, not bolted on: what the user is told, what they agree to, how that agreement is recorded, and how they withdraw it later without emailing support and hoping somebody acts on it.
Data minimisation as an architecture decision: store a mathematical template rather than the photograph wherever the use case allows, encrypt it at rest, and keep it out of the general application database.
Matching placed deliberately: on-device keeps the template on the handset and narrows your exposure, while server-side supports recognition across devices and shared hardware. Each carries a different risk profile.
Testing across demographics as standard, not as a favour: behaviour examined across skin tone, age and gender presentation, in the lighting your product runs in, with weak cases written down.
Retention, deletion and human review defined up front: how long a template lives, what a delete request actually removes, and which decisions a person checks before they affect somebody.
One accountable vendor: one contract, one invoice and one entity for legal, security and privacy review to run diligence on, instead of a model vendor, an app team and nobody owning the data.
Why Buyers Screen A Face Recognition Company So Hard
- Ask what is stored. A recoverable face image and an encrypted template are different liabilities, and a vendor who has not made that choice deliberately has made it by accident.
- Ask where matching happens. On-device keeps the biometric on the handset; server-side is sometimes necessary, but it moves your risk into a database somebody has to defend.
- Ask how consent is captured and withdrawn. If withdrawal is a support ticket rather than a function in the product, it will not survive contact with real users.
- Ask how the system was tested across different faces. Behaviour is not uniform across skin tone, age or lighting, and a vendor who never measured it cannot tell you where it degrades.
- Check the exit before you need it. The code, the enrolment records and the key material sit in your own accounts, so changing supplier is administration rather than a rebuild.
- Ask which rules they design around. Biometric data is treated separately under several regimes, so the answer should be an architecture conversation, not a certificate.
How A Face Recognition Project Starts
Every project starts with a free 45-minute scoping session. No slide deck, no sales script. You bring the identity problem you are solving and who your users are; you leave with a build plan and a timeline.
We are selective about new projects and cap how many we run at once. We build consented systems only, so if the brief is covert identification, or inferring something about a person from their face, we will decline it.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Templates & Keys
Named
Delivery Lead
Ready to build a face feature you can stand behind?
What We Build With Faces
The Face Recognition Work We Deliver
Every product is different and the underlying jobs repeat: capture a face, decide what to store, match it against an enrolled record, and give a person a route that does not depend on the camera. These are the builds we deliver most often.
Identity Verification
Onboarding, KYC, document match
Once Only
Access Control
Doors, turnstiles, site gates
Opt-In Access
Attendance Log
Shift start, sites, contractors
Recorded
Device & App Unlock
Local match, PIN fallback path
Stays On The Device
Face Detect
Find, crop, align, quality
Before Match
Liveness & Spoofing
Presentation attack defence
Blocked
Duplicate Enrolment
One person, one record only
Unique
Photo Organisation
Grouping faces in a user's own library
Private
Face API Integration
Vendor SDKs wired in properly
Fully Wired
Deletion & Retention
Expiry, erasure, audit records
Demonstrable
Not sure which of these your product needs? Let us map it.
Common Challenges
Why Do Face Systems Go Wrong?
Six patterns behind almost every face recognition project that has to be rebuilt or quietly withdrawn after launch.
Images Kept Raw
01
The system stores photographs because that was the quickest path, so a breach exposes faces rather than vectors. A template is not recoverable the way an image is, and that difference is the whole risk conversation.
Consent Is A Checkbox
02
Users agreed to a paragraph nobody read, and there is no way to withdraw it. The first person who asks to be removed becomes an engineering project.
Never Tested Broadly
03
The system was validated on the team that built it. It behaves differently in poor light and on faces unlike theirs, and nobody ever measured where.
No Human Review
04
An automated result locks somebody out, denies entry or blocks an account with nobody in the loop and no route to appeal. The first wrong match becomes a complaint nobody can answer.
Deletion Never Built
05
Enrolment was built and erasure was not. Templates sit in backups, caches and a vendor's service, so a deletion request removes one row and leaves the rest behind.
Locked To One Vendor
06
Enrolment data lives inside a third-party service in a format only that service reads. Changing supplier means asking every user to enrol again.
Recognise a few of these? Let us do it properly.
Our Face Tech Services
6 Face Recognition Development Services
Six ways to buy face recognition delivery from one accountable vendor. Run one, or run several in parallel under a single contract.
Custom Face Recognition Builds
01
End-to-end delivery of a defined face recognition product: consent design, enrolment, matching, retention and deletion, with a named lead who reports directly into you.
Face App Development
02
Consumer and workforce apps with a face step built in: capture with quality feedback, on-device matching where the use case allows, and a fallback for users the camera cannot serve.
Biometric Authentication
03
Face as one factor in a login, paired with a second factor and a recovery route, so nobody is locked out of an account because a camera disagreed.
Face Detection API Work
04
Detection and matching APIs integrated properly: quality gates, error handling, rate limits, and a written record of exactly what leaves your infrastructure.
Secure Template Storage
05
The services behind the feature, built by our own API development practice: encrypted template storage, key handling and access logs.
Bias Testing & Assurance
06
Structured evaluation across demographic groups and real lighting conditions, with thresholds tuned per use case and the weak cases documented, not buried.
Not sure which piece you need first? Let us scope it together.
Why Choose Us
What Makes Our Face Recognition Work Different
The details that decide whether a face feature survives a security and a privacy review.
A US Legal Entity
01
Stallyons is registered in Delaware. Your contract, your invoice and your legal recourse sit with a US company, not an unknown one.
Consent By Design
02
What users are told, what they agree to and how they withdraw it are designed with the feature, not written afterwards.
Overlap You Set
03
You choose the hours we share with your working day, and stand-ups, reviews and escalations all happen inside that window.
Tested Across Groups
04
We measure how the system behaves across skin tone, age and lighting, and we tell you where it is weakest instead of quoting one number.
Reviewed Code
05
Every merge is reviewed against an agreed definition of done, on your board, where you can read it yourself.
One Contract
06
One contract covers the engagement, so procurement, legal and finance each deal with a single named counterparty.
Ready to see what a careful face build looks like?
Our Process
From First Call To A Live Face System In Six Steps
A build process that settles consent, storage and deletion before any model is chosen.
Discovery
Understand the users, the risk and the use case
Scoping
Agree what is stored, matched and kept
Design
Consent flows, fallbacks, deletion paths
Contracting
NDA, IP assignment, access and onboarding
Deliver
Built, reviewed and tested on merge
Assure & Tune
Bias testing, thresholds, then live rollout
Want to see how this maps to your roadmap?
Technology Stack
What Our Face Recognition Engineers Use
The models, libraries and services we build face systems on, and the tooling that keeps them honest.
Vision Models
Python Toolchain
PyTorch Vision
TensorFlow & TFX
OpenCV Ops
ONNX Export
Enrolment & Templates
Face Detect
Landmark Align
Liveness Test
Vectors
Encrypted Template
Where It Runs
On-Device iOS
Android Device
Server-Side Matching
Edge Cameras
Secure Enclave Storage
Consent & Rights
Consent Capture
Data Expiry
Delete Path
Audit & Access Logs
Human Review Step
Build & Deliver
Fairness Test Suite
Docker Runs
Threshold Tuning
GitHub Actions / CI
AWS & Cloud Infra
Who We Build This For
Face Recognition Built For Consented Use Cases Only
Eight kinds of product with different users and one shared rule: the person whose face it is knows and agrees.
Banking Onboarding
Identity checks at account sign-up
Workplaces & Offices
Access control, visitor passes
Workforce & Shifts
Attendance on site, contractors
Consumer Devices
App unlock, local match, fallback
Healthcare Patient ID
Record match, duplicate records
Travel & Hotel Check-In
Opt-in check-in and boarding
Events & Ticketing
Entry, re-entry, season passes
Consumer Photo Libraries
Grouping a user's own photos
Working in another sector? See all industries we serve.
How We Compare
Your Face Recognition Options, Compared
An honest look at your four delivery options.
| Capability | Face SDK Vendor | In-House Generalist | Freelance Developer | Stallyons Technologies |
|---|---|---|---|---|
| What actually gets stored | Their format, their rules | ✕ Often raw images | Whatever is quickest | Encrypted templates by default |
| Consent capture and withdrawal | ✕ Out of scope | A notice, not a flow | ✕ Not built | Designed into the product |
| On-device or server matching | Their architecture | Server by default | Server by default | Decided per use case |
| Testing across demographics | Their own materials | ✕ Not measured | ✕ Not measured | Measured and written down |
| Deletion and retention | ✕ Your problem | Partial at best | ✕ Not built | Erasure path built and tested |
| A person in the loop | ✕ Not included | Ad hoc | ✕ None | Review step in the design |
| Enrolment data portability | ✕ Locked to vendor | Your own accounts | Varies | Yours from day one |
See the difference for yourself
Complete Engagement
Everything Included In Your Face Recognition Build
From Scoping to Contracting to Delivery, One Vendor
Here is everything included when you build your face system with us:
One Face Recognition Build Price: No Hidden Fees, No Surprises.
Every face recognition engagement includes all eight components above. One contract, one senior team, one predictable cost, and no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free Biometric Review
A written read on what you store, where matching runs, how consent and deletion work, and where the design is weakest, ordered by what exposes you first.
Included Free
Build Plan And Estimate
A phased build plan with scope, milestones, the integrations it needs and a transparent, itemised estimate for the engagement.
Included Free
Free Vendor Checklist
The questions we would ask any face recognition development company about storage, consent, testing and deletion, so you can test us too.
Included Free
Risk-Free Partnership
Our Face Recognition Promise
We stand behind every engagement with commitments that protect your investment.
01
Scope Agreed First
Scope, model, working hours and cost structure are written down and agreed before contracting, so nothing is discovered later.
02
Built to Last
Senior engineers, code review, automated tests, security and accessibility audits, and clean, documented code you fully own.
03
IP And Access Protected
NDA and IP assignment are signed before access, permissions are scoped per person, and your accounts stay under your control.
Start your face recognition build with confidence, backed by our Triple Protection Guarantee.
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"Stallyons took our Figma design and built it into a live web application, a cognitive game with level-based match play, messaging, a tutorial, and a directory that ranks users nationally. What impressed me most was their grasp of the code behind that logic, and the quality of the experience. Delivered on time with steady updates."
Jerry L.
Founder
PicCiti LLC
"We brought Stallyons in to absorb an overflow of work, and they delivered ten iOS and Android apps, from reporting to geo-location for logistics, plus several backend systems, owning design, development, and app-store submission. Everything stood out: code quality, speed, and reliability. Perfect code, on time, adopted company-wide."
William B.
Director
Amplo Solutions
FAQ
Frequently Asked Face Recognition Questions
A face recognition development company builds the system around the match, not only the match itself. In practice that means designing how consent is captured and withdrawn, deciding whether you store an image or an encrypted template, choosing whether matching runs on the device or on your servers, testing how the system behaves across different faces and lighting, and building retention, deletion and a human review step before launch.
Detection finds that a face is present in an image and where it sits; it does not know whose face it is. Recognition compares a face against enrolled records to answer whether this is a particular person. Detection is an ordinary computer vision task, and that broader vision work sits with our machine learning practice. Recognition creates a biometric identifier tied to an individual, which is why storage, consent and deletion design matters more than the model.
Start from what you are allowed to store and what you must be able to delete, because those two answers shape the whole architecture. Then capture with quality feedback, enrol deliberately with consent recorded, match on the device where the use case allows it, set thresholds per use case, not globally, and give every user a route that works when the camera does not.
Ask what gets stored and in what form. Ask where matching happens and why that choice was made. Ask how a user withdraws consent, and what a deletion request actually removes. Ask how the system was tested across different faces and lighting conditions. A vendor who answers those in engineering terms is a different proposition from one who answers with a demo.
You do, from the first day. The repositories, the enrolment records, the template store and the key material are all registered in your name, and NDA and IP assignment are signed before anyone gets access. Everything produced on the engagement is assigned to you outright, with no licence-back.
On-device keeps the template on the handset, which narrows your exposure and often removes the need for a central biometric store, so it suits unlock and single-user cases. Server-side is needed when somebody must be recognised across devices or on shared hardware. We decide it per use case during scoping and write the reasoning down.
As a design constraint rather than a certificate. Biometric data is treated separately under GDPR, the Illinois and Texas biometric statutes, CCPA and the EU AI Act, so we design around consent, purpose limitation, minimisation, retention limits and human review. We build GDPR-aligned systems and work alongside your own counsel, who makes the legal call.
No. We build consented systems only: access control, identity verification, attendance, device unlock, duplicate-enrolment checks and photo organisation. We decline covert identification, and we do not build tools that infer emotion, ethnicity or other traits from a face. Those uses are restricted in several markets and are the wrong product.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready To Build A Face Feature You Can Defend?
Get a free consultation. We will walk your use case, tell you what you should and should not store, and send a written proposal.
