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Daphnis Labs designs, builds and operates AI systems, digital products and connected platforms with senior engineering ownership.
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Fast ideas become valuable when architecture, ownership and delivery discipline carry them into production.
Daphnis turns ambitious ideas into systems that keep working after the demo.
AI shortens delivery loops. Architecture, data boundaries and review keep that speed useful.
Focused teams combine senior technical direction with visible implementation and launch support.
Clear scope, demos, tests and release gates keep decisions visible from build to handover.
We move quickly without losing clarity, control or ownership.
Every technical decision should connect to a clear business result.
We design the system shape before complexity begins to compound.
Access, permissions and data boundaries are limited by design.
Quality is reviewed through evidence, testing and observable behaviour.
Progress, risks and engineering decisions stay visible throughout the build.
Monitoring, support and operational ownership are considered before launch.
Delivery continues through handover, improvement and accountable operation.
AI speeds up research and implementation. Engineers retain architecture, security and release authority.
GOVERNANCE CONTROLS
AI accelerates every stage. A named engineer owns every gate.
Surfaces context and options.
Chooses the direction.
Drafts scope and edge cases.
Approves before build starts.
Generates code and tests.
Reviews and owns architecture.
Runs checks, summarises changes.
Approves the evidence.
Drafts release notes.
Holds the release gate.
Daphnis can support structured planning, documentation, risk review and implementation paths for teams that need clear engineering artefacts before build work scales.
What our clients value about working with Daphnis Labs.
The team at Daphnis Labs redefined what’s possible for Urbanface. They delivered a bespoke, animation-heavy website that remains incredibly quick and functional. The…
We wanted a unique, 'one-of-a-kind' feel for Glareen, and Daphnis Labs delivered an ecosystem that is both beautiful and technically superior. Their expertise in…
The level of technical depth Daphnis Labs brought to our Game project is unparalleled. While the front-end reel games are visually stunning and highly engaging, the…
Practical perspectives on AI, product engineering, commerce and modern software delivery.

Separate Kimi's API and coding-plan limits, budget cache writes correctly, and evaluate a repository refactor against behavior that must survive the change.
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Budget beyond Mercury 3's launch discount, check its streaming and reasoning defaults, and measure how quickly a support agent produces a correct, usable answer.
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Separate the model's context from each provider's limits, calculate a coding-pilot budget, and test a mobile layout repair before committing to self-hosted hardware.
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Compare recorded and realtime transcription costs, identify launch-documentation conflicts, and test whether a corrected collection request survives the trip from audio to an operations draft.
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Check the released model's token rates and API compatibility, calculate a realistic pilot budget, and evaluate image evidence against changing inventory records.
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Separate GLM-Legal's beta access from unconfirmed API terms, interpret its independent benchmark results, and test document evidence with a reviewer-inclusive cost model.
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Check the October 17 model update, peak and off-peak token rates, and new search charges, then evaluate an agent against versioned API evidence.
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Understand Timestral's time-step limits and weight license, estimate a local forecasting job, and design a retail backtest that cannot see future sales.
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Check Sonnet 5.5's token costs, thinking settings and conversation requirements, then evaluate coding changes against a concrete merchant-sorting fixture.
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Check Gemini 4 Argon's access rules, shared token budget and launch pricing, then use a controlled supplier-data evaluation to plan an API migration.
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