Agency Comparison
Digica vs Fuzzy Labs
Two top-rated AI Development agencies in Manchester.
Comparison updated August 2026
Side by side
Published data on both agencies. Where one side measurably differs, the dot marks it: the higher score, the lower published rate, the larger team, the more recent review. Which of those is an advantage depends on your brief.
| Attribute | Digica | Fuzzy Labs |
|---|---|---|
| Editorial score | Higher score: 57.8/100 | 56.3/100 |
| Hourly rate | Contact for rates | Contact for rates |
| Team size | Larger team: 50-249 | |
| Location | Manchester, United Kingdom | Manchester, United Kingdom |
| Founded | — | — |
| Last reviewed | Aug 4, 2026 | Aug 4, 2026 |
What we said about each
Our take on Digica
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…
Reviewed Aug 4, 2026
Read the full assessment →Our take on Fuzzy Labs
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…
Reviewed Aug 4, 2026
Read the full assessment →Best for
Where each agency measurably leads on the published data — team capacity, declared specializations, editorial scoring, and rates where both publish them. Use these to match an agency to your project priorities.
Choose Digica if you need
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in Healthcare
Choose Fuzzy Labs if you need
- closer collaboration at a smaller team scale
- disclosed specialization in SaaS & B2B
Strengths and watch-outs
Both upsides and risks, straight from our editorial assessments.
Digica
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
Watch-outs
- The blue-chip logo wall is not connected to the case studies—ask which names map to which engagements
- No published rates or founding year
Fuzzy Labs
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
Watch-outs
- 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
Service coverage
Both agencies cover the same services — on this axis there is nothing to separate them, so decide on the evidence behind the work rather than its labels.
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Frequently asked questions
- 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 57.8/100, 1.5 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.
Read the full reviews
A side-by-side is a starting point. The full assessments carry the portfolio analysis, documented engagements, and the evidence behind every strength and watch-out above.