Dathelyn
A global Team-as-a-Service studio — hybrid engineering teams that design, build, and ship what comes next.
Engineers across multiple time zones, working as one team on your product.
Direct access to the people building your software — no black-box outsourcing.
Small hybrid teams that move quickly from scope to working software.
Experienced leads stay close to the work and keep quality consistent.
45+
Projects Delivered
99%
Client Satisfaction
24/7
Support Available
Our Services
Hybrid Team-as-a-Service delivery across AI, platform engineering, product, data, and security — the same practices we use on Meridian Pay, Northline CRM, and our other engagements.
Build production GenAI apps — RAG pipelines, agent workflows, and automation that plug into your existing tools.
Key Technologies
Build secure on-chain applications across Ethereum and L2 networks—including wallets, marketplaces, and verifiable workflows.
Key Technologies
Modernize delivery with Kubernetes, GitOps, and internal platforms that make shipping safer and faster.
Key Technologies
Build lakehouse and real-time pipelines that feed product analytics and AI workloads cleanly.
Key Technologies
From MVP to multi-tenant SaaS — TypeScript-first web products with solid APIs and design systems.
Key Technologies
Embed Zero Trust, AppSec, and compliance into delivery — so security keeps pace with shipping.
Key Technologies
Our Portfolio
Representative engagements that show how we design, build, and hand off production software.
Digital banking web app for a regional neobank, featuring transfers, card controls, fraud review workflows, and a real-time ledger system.
Technologies
Key Metrics
8-month engagement
Learning platform for a training provider, featuring live classes, progress tracking, and an AI study coach that answers questions using course materials.
Technologies
Key Metrics
7-month engagement
Sales workspace for a mid-market SaaS company: pipeline hygiene, meeting summaries, and draft follow-ups from CRM context.
Technologies
Key Metrics
6-month engagement
Shipment provenance platform for an import-export operator, with IoT checkpoint data stored off-chain, hashes anchored to an EVM-compatible network, and an operations dashboard for exception management.
Technologies
Key Metrics
10-month engagement
How we work
45+
Product, platform, and AI engagements across fintech, edtech, B2B, and logistics.
99%
Measured from engagement close-outs and ongoing retainers.
24/7
Follow-the-sun coverage for production systems when the contract includes it.
2-wk
Working software every two weeks — demos, not slide decks.
R&D
Client work sustains the studio. Alongside delivery, we run focused R&D initiatives to test new ideas before bringing them into production.
These are active prototypes and design studies—not product announcements. Some may become standalone products; others will evolve into reusable patterns for client engagements.
Active Tracks
Internal Alpha — 2026 Q4
AI-assisted DevOps
An internal tool that analyzes CI/CD signals, recommends rollout steps, and drafts incident timelines for hybrid-cloud services. Initially built for our own delivery teams.
Goal
Reduce release overhead and make production incidents easier to investigate and reconstruct.
Internal Pilot — 2027 Q1
AI-assisted product design
A collaborative workflow where designers and engineers use generative models to refine interfaces, API contracts, and user flows—with human review at every stage.
Goal
Accelerate early product iteration without bypassing technical and architectural review.
Research Prototype — 2027 Q2
Privacy-preserving model training
A research prototype that enables hospitals and laboratories to collaboratively improve diagnostic models through federated learning. Patient data remains local, while privacy-protected model updates are securely aggregated.
Goal
Evaluate whether collaborative training can improve model quality without centralizing sensitive patient records.
Prototype — 2027 Q2
Verified GPU and CPU pools
A marketplace prototype where idle compute capacity can be contributed to shared AI workloads. Usage is verified through workload attestations, with metering and settlement coordinated through smart contracts.
Goal
Evaluate whether shared, verifiable compute capacity can reduce burst-training costs for small teams.
Design Study — 2027 Q3
Secure infrastructure for web agents
A reference architecture for reasoning agents that can call APIs, use scoped credentials, and complete multi-step web tasks with audit trails and explicit human approval gates.
Goal
Define a practical safety pattern for agents that act on the web without creating unreviewed side effects.
If one of these tracks aligns with your roadmap, we can share its current status and explore how it might support a paid engagement.
Discuss an R&D track →