# Digica vs Fuzzy Labs

> Two top-rated AI Development agencies in Manchester. Side by side: published rates, team size, service and industry coverage, and the curator's assessment of both — pairings are never sold.

Canonical page: https://www.aiagencies.eu/compare/digica-vs-fuzzy-labs
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Digica | Fuzzy Labs |
|---|---|---|
| Editorial score | 60/100 | 56.3/100 |
| Hourly rate | €45-85/hr (directory-listed) | not published |
| Team size | 50-249 | — |
| Location | Manchester, United Kingdom | Manchester, United Kingdom |
| Founded | 2009 | — |
| Last reviewed | 2026-08-04 | 2026-08-04 |
| Services | AI Development, AI Consulting | AI Development, AI Consulting |
| Industries | Manufacturing, Healthcare | SaaS & B2B, Manufacturing |

## Our take on Digica
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

The preventive-maintenance work is documented the way an engineering buyer would want it: a neural network with XGBoost decision trees and SHAP for explainability, predicting mobile-device failure at 91% accuracy.

Sensor-fusion work covers training convolutional networks on synthetic images and combining RGB with near-infrared, and there is published work running language models on Arm CPUs for edge devices—a constraint most AI agencies never encounter.

Clients are named as logos rather than attached to engagements, so the technical evidence is strong and the commercial evidence is not.

Best for manufacturers and device makers who need models running on hardware rather than in a cloud region, and who will recognize the difference immediately.

### Key strengths
- Explainability is built in, not bolted on—SHAP appears in the published method, which regulated buyers will need
- Embedded and edge deployment is a stated specializm, including LLMs on Arm CPUs
- 80+ data scientists and engineers, with offices in the UK, Poland and Berlin

### Good to know
- The blue-chip logo wall is not connected to the case studies—ask which names map to which engagements
- No founding year published, and the €45–85/h shown here is a third-party directory figure, not Digica's own

## Our take on Fuzzy Labs
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

The Zally case study is the kind most agencies avoid publishing: a specific number attached to a specific bottleneck, model deployment cut from two days to five minutes by a custom MLOps pipeline.

The PEBL work puts computer vision on edge devices for seaweed farm monitoring, which is a harder engineering problem than it sounds—inference has to run where there is no reliable connection.

Open-source MLOps is the stated specializm and the published work supports it, though a third case study for a US sports broadcaster is anonymized and no team size or founding year is published.

The weaker fit is a buyer who wants a model demonstrated rather than operated; this practice is built around what happens after the notebook works.

### Key strengths
- Publishes a hard before-and-after metric—two days to five minutes on deployment—which almost no agency on this register does
- Edge deployment for PEBL shows inference running outside a data center, not just in one
- Open-source tooling by preference, so the pipeline stays inspectable and portable after handover

### Good to know
- No published team size or founding year—ask about capacity and who would run your engagement
- Specializm is the production path rather than model research; if you need novel modeling, check the fit

## Which to choose
Choose Digica for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in Healthcare
- a higher overall editorial score

Choose Fuzzy Labs for:
- closer collaboration at a smaller team scale
- disclosed specialization in SaaS & B2B

## Questions buyers ask

### Which has more Manufacturing experience, Digica or Fuzzy Labs?
Both agencies show documented Manufacturing work and have similar industry breadth. Compare directly on the agency profiles: Digica and Fuzzy Labs.

### Which scores higher overall, Digica or Fuzzy Labs?
Digica scores 60/100, 3.7 points higher than Fuzzy Labs at 56.3/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both Digica and Fuzzy Labs?
Consider running parallel discovery briefs with Digica and Fuzzy Labs if your project spans multiple workstreams, you want competitive proposals to compare scope and approach, or you're undecided between the specialization angles each brings (see the Best for cards above). Most engagements ultimately go with one — but the parallel-brief phase is a low-cost way to validate fit.

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Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates