AI Services From Strategy to Shipped Systems

Devlyn AI services include strategy and readiness, AI data engineering, RAG systems, AI agents and workflow automation, conversational AI, enterprise integration, MLOps, observability, cost optimization, security testing, governance, product UX, synthetic data, and rescue work. The scope is selected based on the production outcome and risk profile, not a generic AI package.

Start with the blocker. If the use case is unclear, choose strategy and readiness. If answers are unreliable, look at data engineering or RAG. If AI must act across tools, choose agents or enterprise integration. If the system is already live, observability, cost, security, governance, or rescue may be the right first step.

Yes. We audit the existing proof of concept, source data, architecture, prompts, retrieval, model choices, integrations, security posture, eval coverage, cost profile, and operational ownership. Then we recommend whether to harden, narrow, rebuild, or stop the work.

Yes. We build RAG systems with ingestion, retrieval, reranking, citations, evaluation, and observability. We also build AI agents with scoped tools, state, approvals, retries, audit logs, and escalation paths where production workflows require action.

Yes. We can work inside your repositories, backlog, communication channels, code review process, release workflow, and security rules. Your team keeps product direction and final decisions while we own the agreed delivery scope.

The quality bar depends on the service. For RAG, we look at retrieval relevance, groundedness, citation support, freshness, and refusal behavior. For agents, we measure workflow completion, tool safety, approval behavior, and traceability. For production systems, we also track latency, cost, errors, feedback, and release regressions.

We define data boundaries before implementation: what can enter prompts, embeddings, logs, training sets, evals, tools, and third-party providers. We can also test for prompt injection, sensitive information exposure, RAG leakage, tool misuse, and excessive agency.

Your organization retains ownership of source code, repositories, product direction, priorities, and final decisions according to the engagement terms. We support NDA, scoped access, documentation, and handover so your team is not locked into unclear ownership.

Yes, but only after instrumentation. We connect spend to models, prompts, users, workflows, providers, caching, routing, and quality signals before recommending changes. The goal is controlled cost, not weaker AI behavior.

AI work can be structured as a discovery sprint, fixed-scope implementation, managed delivery engagement, embedded specialist team, or AI engineering pod. The right model depends on risk, urgency, internal ownership, and how much execution responsibility you want Devlyn to carry.

The first step can usually begin once the outcome, stakeholders, access constraints, and commercial terms are clear. We avoid promising production timelines before reviewing the workflow, data readiness, integration complexity, and security requirements.

Yes. Post-launch support can include monitoring, eval reviews, prompt and retrieval updates, cost review, incident support, security retesting, model-routing changes, and roadmap improvements. The support model is defined before launch so ownership is clear.

Freelancers can help with isolated tasks, and generic vendors may deliver broad prototypes. Devlyn focuses on accountable production delivery: scope clarity, senior review, quality checks, security, observability, documentation, and a handover path your team can trust.

Share the workflow you want to improve, current systems, source data, target users, known risks, timeline, internal team structure, and what has already been tried. That lets us recommend the right AI service path instead of forcing a generic solution.