aphrodyte
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AI Agent Development Services

Custom AI agents that never ship unsupervised

We design and build bounded AI agents for real marketing and ops work, with human approval gates on anything irreversible. Senior engineers own the architecture and review every merge before it touches production.

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Free proposal · Reply within one business day · No long-term contract.
What's included Expert-led
  • Workflow mapping & task scoping
  • Agent architecture on Claude Agent SDK
  • Human-in-the-loop control layer
  • Integrations via MCP servers
  • Eval suite & observability
  • Cost governance & handover
6 areas, owned by a senior SEO — reported live in Aphra.
Yours
Code, hosting & data ownership
0
Unsupervised production actions
Gated
Every irreversible action
45-60d
To a working production agent
Our approach

Most "AI agents" are Zapier templates in a trench coat

The market is full of no-code resellers wrapping the same RAG template and calling it an agent. It demos beautifully and collapses on real inputs, because the systems around the model, durable state, cost caps, permissions, evals, are missing. We build the opposite: engineered agents where senior humans own the architecture and every gate, and AI accelerates the grunt work under supervision.

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What our senior experts own

Every decision. No exceptions.

  • Architecture, agent design, and the human-in-the-loop control layer
  • Reviewing and approving every merge before it reaches production
  • Which tasks the agent touches versus which stay human-gated
  • Integrations, eval suites, and cost governance built for real inputs

What AI accelerates

Grunt work only. Never ships alone.

  • Mapping workflows and drafting first-pass agent scaffolding
  • Wrapping your APIs and generating integration boilerplate
  • Running research, monitoring, and drafting into staging areas
  • Flagging regressions and drift through automated eval hooks
What's included

Everything your ai agent development needs — in one team.

Workflow mapping & task scoping

We break your target process into microtasks and decide, with you, which the agent touches and which stay human, because data prep and integration is often 50 to 70 percent of the work.

Agent architecture on Claude Agent SDK

We design the agent loop, session persistence, subagents, tools, and MCP servers around your systems rather than forcing your work into a template.

Human-in-the-loop control layer

Low-risk, reversible actions auto-run; anything irreversible, external, or financial surfaces an approval packet with intent, blast radius, and reasoning before it proceeds.

Integrations via MCP servers

We wrap your CRM, analytics, CMS, and data warehouse APIs as MCP servers so the agent works against your real stack, including the messy edge cases.

Eval suite & observability

Offline evals catch regressions and online hooks catch drift, prompt injection, and bad tool calls, all visible in a full trace of every LLM turn and tool invocation.

Cost governance & handover

Circuit breakers, token budgets, and per-run cost caps ship with the agent, alongside documentation, a runbook, and the codebase itself, hosted in your own accounts.

How we work

Expert-led. AI-accelerated.

SEO is a craft — and it's ours. AI just lets our strategists do more of it, faster.

STEP 01

Experts set the strategy

A senior strategist digs into your site, market, and competitors and builds the plan — the judgment AI can't replace.

STEP 02

AI accelerates execution

Audits, research, clustering, first drafts — AI handles the heavy, repetitive work in a fraction of the time, directed by our team.

STEP 03

Experts refine & ship

Every deliverable is reviewed, sharpened, and signed off by a human before it touches your site. Nothing auto-publishes.

What you get

Every month — in your inbox and in Aphra.

No black boxes. Concrete deliverables you can point to, plus the reporting to prove it worked.

  • Workflow map with human-versus-agent task boundaries
  • Agent architecture spec (loop, subagents, tools, skills)
  • MCP servers wrapping your existing system APIs
  • Human-in-the-loop approval gates with audit trail
  • Offline eval suite plus online evaluation hooks
  • Full observability: per-run trace tree and token counts
  • Cost governance: circuit breakers, budgets, per-run caps
  • Rollback and checkpoint logic for denied approvals
  • Documentation, runbook, and the codebase you own
What good looks like

Outcomes, not just activity.

Grunt work off your team's plate

Research, audits, monitoring, and first-draft reporting run continuously so your specialists spend their hours on judgment, not busywork.

Automation you can actually trust

Because irreversible actions are gated and every run is traceable, you get speed without handing high-blast-radius decisions to an unsupervised model.

An asset you own outright

The code, hosting, and data stay in your accounts, so there is no lock-in tax and no scramble to rebuild if the engagement ever ends.

Why Aphrodyte

Real SEO expertise. AI as the edge.

Expert-led

Real SEO strategists own your account — the strategy, the calls, the results. AI is our tool, never your point of contact.

AI-accelerated

We do in days what used to take weeks, so your budget buys senior expertise, not billable busywork.

All in Aphra

Rankings, traffic, leads, and every task we've shipped — live in your client platform, no chasing required.

FAQ

Questions, answered.

Do I even need an agent, or is this just automation?
Honestly, sometimes plain automation is the right answer, and we'll tell you when it is. If a task is deterministic and rule-based, a script is cheaper and more reliable than a genAI agent. Agents earn their keep when there's genuine ambiguity, language understanding, or multi-step reasoning involved. We scope that in the first conversation before anyone writes a proposal.
Who owns the code, the data, and the outputs?
You do, completely. The codebase is yours, it's hosted in your own cloud and accounts, and your data stays in your environment. Nothing you give us, prompts, inputs, or outputs, is used to train any model, ours or a vendor's. There are no quiet reuse or re-license clauses in our contracts.
How long until it's actually working in production?
A simple, bounded single agent is typically 2 to 6 weeks; a task-specific agent with full integrations runs 4 to 12 weeks depending on how many systems it touches. For most marketing and ops workflows you'll see a working production agent in roughly 45 to 60 days, then we harden and expand from there.
How do you price this?
For well-defined scope we quote a fixed project fee; for ongoing optimization we use a monthly retainer. Token and API costs are always passed through transparently and kept separate from our build fee, so you never get a surprise usage bill buried inside our invoice. We'll walk you through the model that fits your scope before you commit.
What exactly is the human doing versus the AI?
Senior engineers own the architecture, every design decision, and every merge into production, no AI-generated pull request ships without a human reviewing it for security, business logic, and edge cases. AI accelerates the grunt work: mapping workflows, drafting scaffolding, wrapping APIs, running research, and flagging regressions. Humans decide; AI drafts.
Will it really work in production, or just in the demo?
The demo-to-production gap is the single most under-budgeted reality in this field. Demos use clean inputs; production faces typos, missing fields, ambiguous references, and APIs that rate-limit or silently change schema. We plan for that with eval suites, full observability, and human gates, and we budget roughly 10 to 15 percent of the build for testing and red-teaming, not promises.
What does "never acts unsupervised" mean concretely?
We apply one rule: if an action is low-risk and fully reversible, it auto-runs; otherwise it's gated. Read-only lookups, summaries, and drafts into a staging area run freely. Anything irreversible, any external communication, and any financial impact surfaces an approval packet with the agent's intent, the affected resources, its reasoning, and the blast radius before a human approves it.
How do I know what the agent did and what it cost?
Every run produces a full trace: each LLM turn, each tool call with its arguments and results, and token counts per step, grouped by session. Cost governance, circuit breakers, token budgets, and per-run caps, is built in from day one. You see progress, approvals, and results reported live in Aphra.
What happens when the engagement ends?
Nothing breaks and nothing gets held hostage. Because the code, hosting, and data already live in your accounts, you keep running the system exactly as-is. We hand over documentation, a runbook, and the full codebase so your team, or any other, can maintain and extend it. Portability is designed in, not bolted on.
What are the true ongoing costs after launch?
Agents aren't build-once-and-walk-away. Expect recurring token and API usage, plus monitoring, debugging, and occasional prompt tuning as your data and systems evolve. We're upfront that the initial build is only part of the total cost of ownership, and we'll give you honest ranges for maintenance rather than pretending it's zero.
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