Deeplocal vs ANML: full comparison for 2026
Quick verdict
Deeplocal (3.9/5) edges ahead of ANML (3.7/5) overall. Deeplocal is the better choice for brands needing working hardware prototypes, not just software. ANML is the stronger option for B2B, healthcare, AI startups wanting a small senior team. The right choice depends on your project size, budget, and required tech stack.
Deeplocal vs ANML: head-to-head summary
| Criterion | Deeplocal | ANML |
|---|---|---|
| Founded | 2006 | 2012 |
| HQ | Pittsburgh, PA, USA | United States (specific city not published) |
| Team size | ~75 | ~10 (per LinkedIn) |
| Rating | 3.9 / 5 | 3.7 / 5 |
| Primary differentiator | A Carnegie Mellon-spinoff studio that engineers and builds working hardware prototypes in-house, not just software mockups | One of the smallest teams in this pool, offering the highest senior-to-client ratio for B2B, healthcare, and AI work |
| Pricing model | Project-based (not published) | Fixed project |
| Min. engagement | Not published | Not published |
| Primary tech stack | Figma, React, Arduino/embedded systems | Figma, Sketch |
| Industries served | Consumer products, Enterprise transformation, Automotive & mobility | Healthcare, SaaS / B2B software |
Deeplocal vs ANML: overview
Deeplocal
Deeplocal spun out of Carnegie Mellon University in Pittsburgh in 2006, building a studio around robotics, hardware development, electrical engineering, and industrial design alongside standard software and creative work. WPP Digital acquired the studio in 2017. At around 75 people, Deeplocal does most of its prototyping and production in-house, which shows up in a client list — Google, Netflix, Airbnb, Lyft, Nike, Toyota, Volkswagen, PNC Bank — that regularly asks for physical, working prototypes rather than mockups.
ANML
ANML (Animal) has operated since 2012 with a team so small — LinkedIn lists roughly 10 employees — that it barely registers next to the enterprise names in this pool. No specific HQ city appears in available sources, so it's treated here simply as US-based. The trade-off for that scale is direct access: clients work with senior people from day one, on a deliberately narrow set of engagements concentrated in B2B, healthcare, and AI.
Services and capabilities: Deeplocal vs ANML
| Capability | Deeplocal | ANML |
|---|---|---|
| Product design | ✓ | ✓ |
| UX research | ✗ | ✓ |
| Branding | ✗ | ✗ |
| Web development | ✓ | ✗ |
| Mobile development | ✗ | ✗ |
| Design systems | ✗ | ✗ |
Tech stack comparison: Deeplocal vs ANML
| Framework / platform | Deeplocal | ANML |
|---|---|---|
| Figma | ✓ | ✓ |
| Webflow | N/A | N/A |
| React | ✓ | N/A |
| Framer | N/A | N/A |
| Adobe Creative Suite | N/A | N/A |
| Design systems/Storybook | N/A | N/A |
| After Effects | N/A | N/A |
Pricing comparison: Deeplocal vs ANML
| Criterion | Deeplocal | ANML |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Dedicated team | Fixed project |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Deeplocal vs ANML
| Dimension | Deeplocal | ANML |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Consumer products, Enterprise transformation, Automotive & mobility | Healthcare, SaaS / B2B software |
| Best use cases | Connected-hardware or physical prototype design for a brand campaign, Automotive or consumer-electronics product design requiring in-house engineering | B2B SaaS product design for a small, senior team, Healthcare product UX with a boutique company |
| Typical project type | Fixed project | Fixed project |
Deeplocal vs ANML: pros and cons
| Deeplocal | |
|---|---|
| + | In-house robotics, electrical engineering, and industrial design capability alongside software and creative work |
| + | Client list (Google, Netflix, Airbnb, Lyft, Nike, Toyota, Volkswagen, PNC Bank) spans tech, consumer, and automotive |
| + | WPP Digital backing since 2017 provides access to a larger global network when needed |
| + | Two decades of continuous operation since its 2006 Carnegie Mellon spinout |
| - | A ~75-person team caps capacity for very large, multi-workstream programs |
| - | Hardware-and-prototype focus is a narrower fit for clients needing pure software UI/UX work |
| - | Pricing and minimum engagement size aren't published |
| ANML | |
|---|---|
| + | 13 years of continuous operation since 2012 |
| + | Very small (~10 person) team means direct access to senior designers on every project |
| + | Focused specifically on B2B, healthcare, and AI rather than spreading across unrelated verticals |
| + | Boutique scale keeps overhead — and likely pricing — lower than mid-size companies |
| - | HQ city is not explicitly published in available sources, making delivery-location verification harder |
| - | A ~10-person team caps capacity for large or multi-workstream programs |
| - | Minimum engagement is not published, so budget planning requires a direct conversation |
Who should choose Deeplocal?
A typical fit: connected-hardware or physical prototype design for a brand campaign.
A Carnegie Mellon-spinoff studio that engineers and builds working hardware prototypes in-house, not just software mockups. Minimum engagement starts at Not published. Works best with clients in Consumer products, Enterprise transformation, Automotive & mobility.
Who should choose ANML?
A typical fit: B2B SaaS product design for a small, senior team.
One of the smallest teams in this pool, offering the highest senior-to-client ratio for B2B, healthcare, and AI work. Minimum engagement starts at Not published. Works best with clients in Healthcare, SaaS / B2B software.
Decision matrix: Deeplocal vs ANML
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Deeplocal |
| You need a large dedicated team for an ongoing programme | Deeplocal |
| Your budget is at the lower end | Compare: Deeplocal (Not published) vs ANML (Not published) |
| You need specialist depth in a specific vertical | Deeplocal |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Deeplocal vs ANML
| Use case | Deeplocal fit | ANML fit | Winner |
|---|---|---|---|
| Connected-hardware or physical prototype design for a brand campaign | Strong | Limited | Deeplocal |
| Automotive or consumer-electronics product design requiring in-house engineering | Strong | Limited | Deeplocal |
| B2B SaaS product design for a small, senior team | Limited | Strong | ANML |
| Healthcare product UX with a boutique company | Limited | Strong | ANML |
| Fixed-price product build | Limited | Limited | Both equally |
| Dedicated design team augmentation | Limited | Limited | Both equally |
Verdict: Deeplocal vs ANML
Deeplocal (3.9/5) is the stronger overall choice for most Product Design projects. A Carnegie Mellon-spinoff studio that engineers and builds working hardware prototypes in-house, not just software mockups.
ANML (3.7/5) is worth a look if you need healthcare product UX with a boutique company. If your situation matches that, ANML is a competitive option.
Related comparisons
Deeplocal vs ANML FAQ
Is Deeplocal better than ANML?
Deeplocal (3.9/5) scores higher overall, but "better" depends on your use case. Deeplocal's strongest advantage: in-house robotics, electrical engineering, and industrial design capability alongside software and creative work. ANML's strongest advantage: 13 years of continuous operation since 2012.
How do Deeplocal and ANML differ in pricing?
Deeplocal uses project-based (not published) pricing with a minimum engagement of Not published. ANML uses fixed project pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Deeplocal or ANML?
Deeplocal is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Deeplocal and ANML?
Deeplocal's primary differentiator is: a Carnegie Mellon-spinoff studio that engineers and builds working hardware prototypes in-house, not just software mockups. ANML's primary differentiator is: one of the smallest teams in this pool, offering the highest senior-to-client ratio for B2B, healthcare, and AI work. They also differ in team size (~75 vs ~10 (per LinkedIn)), minimum engagement (Not published vs Not published), and primary industries served (Consumer products, Enterprise transformation vs Healthcare, SaaS / B2B software).
Verify all details directly with each company before making a decision.