Services
Pricing, timelines, safety, data ownership, the tech stack and what working together actually looks like. Real questions from scoping calls.
Book a free 30-minute scoping call. Within a few days you get a one-page scope with the price, the timeline and what gets delivered. Then you decide: go ahead, or keep the scope and walk away. Nothing is billed before you say yes.
We talk about the workflow you want to change, who does it today, which systems it touches and what "done" looks like as a number. I tell you honestly whether an AI agent is the right tool, which offer fits, and roughly what it would cost. No pitch deck.
No. It helps to know which task you want to automate and roughly how often it happens, but you can come with just the problem. If you have example data, screenshots or an existing agent that is misbehaving, bring them.
Yes. The one-page scope is yours to keep even if you never hire me. Some teams use it to brief their own engineers, and that is fine.
It depends on where you are. Validating an idea: Vibe-Built MVP. Building a real product: AI Product Sprint or AI Agent Development. An agent that is live but failing: Production Audit + Rescue. Ongoing AI leadership: Agentic Product Retainer or a fractional forward deployed engineer. An AI bill that is too high: LLM Cost Optimization.
Audits usually start within 5 business days of the scoping call. Builds start once the scope is signed; I give you a firm start date on the call, based on what is already booked.
Yes. I am happy to sign your NDA before you share code, data or anything about your customers.
An AI agent is software that uses a language model to decide what to do next, then takes actions through tools: it can look up an order, update a record, draft an email or call an API. A chatbot answers questions; an agent changes something in your systems.
Agents that take real work off a team: customer support and operations agents, ERP and back-office agents, sales research and outreach agents, lead qualification and routing, and internal assistants over company knowledge. Most of them read from and write to systems you already use.
A workflow follows a fixed path: step A, then B, then C. An agent decides the next step based on what it just saw. Workflows are cheaper and more predictable, so I use them wherever the path is known, and agents only where the input is messy or needs judgment.
Default to a single agent. Multi-agent systems help when the work splits into clear specialties, like a researcher, a writer and a reviewer, or when parallel work saves time. They also add coordination bugs and token cost, so I only recommend them when a single agent measurably falls short.
Yes, when a chatbot is the right tool: website assistants, WhatsApp support, and assistants that answer from your documents. Where the bot should also take actions, like booking or updating an order, it becomes an agent with approval steps.
Yes. I work inside your codebase and your cloud, add the agent or AI feature behind your existing auth, and ship it with tests and tracing so your team can maintain it after I leave.
Yes. That is the Production Audit + Rescue offer: a flat-fee, 2-week audit of cost, quality, evals and failure patterns, ranked by impact. If you want it fixed, the audit fee credits toward the rescue.
Yes, when voice is the right channel, such as phone support or appointment booking. I also designed AI contact centers on AWS and Azure for Fortune 500 companies before building agents full time.
An engineer who works directly with the people who have the problem, inside their systems, and writes the production code that solves it, for part of the week or a fixed project instead of a full-time hire.
A working AI product with real users in about 3 weeks: one user journey end to end, with auth, the AI integration and polished UX, hosted and in your GitHub. It is built for founders who need to prove the idea before investing in a full build.
Both, but always tied to building. I do the research, the product spec, the architecture and the UX, then write the code. Pure advice with no build is rarely what I recommend.
AI agent development starts at $15,000 for 3 to 6 weeks. A Vibe-Built MVP is $15,000 to $30,000 in about 3 weeks, and an AI Product Sprint for a production agent with human approval, evals and tracing is $30,000 to $60,000 in 4 to 6 weeks.
Vibe-Built MVP: $15-30K, about 3 weeks. AI Product Sprint: $30-60K, 4-6 weeks. AI Agent Development: from $15K, 3-6 weeks. Production Audit: $3,500 flat, 2 weeks, with rescue from $25K. LangChain and LangGraph engineering: from $8K. LLM Cost Optimization: from $3,500. AI Automation Consulting: $3,500 assessment, builds typically $8-25K. Agentic Product Retainer and fractional forward deployed engineer: $15-30K a month.
Fixed prices. The price is agreed in the scope before any work starts, and I don't bill by the hour. Retainers are a fixed monthly fee.
Discovery, architecture, the build, an eval suite, tracing and monitoring, documentation and a handover, plus 30 days of bug-fix support after launch. Model API usage and hosting are paid directly by you, in your own accounts.
Fixed-price milestones, invoiced in USD by bank transfer. Upwork works too if you prefer its escrow. UAE clients can be invoiced in AED and Canadian clients in CAD.
We pause, re-scope together, and you decide whether to continue at the new price and timeline. Nothing changes without your written approval.
Yes, two: the model provider's API usage and hosting, both billed to your own accounts. Part of every build is keeping those costs predictable, with caching and model routing where they help. Ongoing support from me is optional.
Yes. The Production Audit and the AI Automation assessment are both $3,500 flat, and both credit toward the build if you go ahead.
No. Engagements are cash only, with no equity and no board seat, which keeps incentives simple.
A validated MVP ships in about 3 weeks. A production agent with human-in-the-loop approval, evals and tracing takes 4 to 6 weeks. An audit takes 2 weeks. You get the timeline in the scope before any billing starts.
You get a short written update, working software at a weekly demo, and decisions and questions in writing, so nothing waits for the next meeting. You can follow the traces and eval scores yourself at any time.
One person who can make decisions, access to the systems the agent will use, a sample of real data or examples, and about 30 minutes a week for the demo and feedback.
30 days of bug-fix support are included. After that you can take it over with your own team using the docs and evals I hand over, or keep me on through a retainer.
In stages: first against a test set of real examples, then to a small share of real traffic with approval on risky actions, then wider as the numbers hold. Every change reruns the evals before it ships.
A fixed-price build can be paused at a milestone and re-scoped. Retainers have a 3-month minimum and can then be cancelled with 90 days' notice, or scaled down at the start of any month.
Three layers: structured outputs instead of free text where possible, an eval suite that scores every change against a fixed set of real test cases, and human approval on high-risk actions. Where an agent quotes a number or a source, it is checked against the data, not trusted.
I build a set of golden test cases from real examples, usually 20 to 30 to start and more over time, and run them automatically on every prompt, model or code change. No eval suite means no way to know you broke something.
It means the agent pauses and waits for a person before a risky action, such as a refund, a record change or an outgoing email. You need it wherever a mistake would cost money, customers or trust.
Yes, with the right guardrails: approval before writes, each agent limited to the tools and records it needs, a check for duplicates, reading the record back after every write, and undo as a plan you approve. That is how the Odoo ERP agent for a Dubai client works.
Every step is traced, usually in LangSmith or LangFuse, so any decision can be reconstructed. Cost, latency and quality are tracked per agent, and alerts fire on the problems a person can act on.
The eval suite catches it. Model and prompt changes run against the same test cases before they ship, and a change that scores worse does not go live.
Everything an agent reads from outside, such as web pages, emails or documents, is treated as data and never as instructions. Tools are limited per agent, and actions that matter go through code checks and approval that no text can talk its way past.
You do. The code lives in your GitHub from the first commit, and the contract assigns all IP to you, including prompts, evals and documentation.
In your own cloud account, such as AWS, GCP or Azure. Data goes only to the model provider you approve.
Your data is sent only to the model provider you approve, under its business or API terms. We choose providers and settings that don't train on your data, or use a self-hosted model where that is required.
Yes. The agent runs inside your compliant environment, we map what it may read and write in week one, and every action is logged. Your compliance or legal team signs off on the design; I build to it.
Yes. When data can't leave your environment, or cost at volume matters, I use open models such as Qwen or DeepSeek, hosted in your infrastructure.
LangGraph by default for anything with state, branches or human approval, LangChain for simpler chains and retrieval, and plain Python where a framework would add weight without value.
LangChain is fine for single-prompt chains, retrieval and simple tool use. LangGraph is better as soon as you need state that survives between steps, conditional branches or human approval.
Whichever fits the task and the budget: Claude, OpenAI, Gemini and DeepSeek models, and open models when they need to be self-hosted. Easy requests go to cheaper models and hard ones to stronger models, decided by eval scores.
Yes. New builds are MCP-first where it fits, so your tools are exposed once and reused by any agent, instead of a custom integration for every pair.
Usually Postgres with pgvector, combining vector search with full-text search so the agent finds both paraphrases and exact terms like product codes, then re-ranking the results. Fewer, better chunks mean better answers and a smaller bill.
Both. Python for data-heavy pipelines and most agent back ends, TypeScript when the agent lives inside a Next.js or Node app. I match your existing stack.
No. I build in code, in your repository, with tests and tracing, so the result is versioned, reviewable and yours to maintain.
AWS, Google Cloud and Azure, plus platforms like Vercel, Render and Supabase for app layers. The agent is deployed in your accounts, not mine.
Usually three reasons: an expensive model answers questions a cheaper one could handle, the same questions are paid for again and again with no cache, and prompts carry far more context than they need.
A 40 to 70 percent reduction is normal on support and retrieval workloads, less on pure generation. On one US client's product, capping the model's hidden reasoning made each AI pitch 20 times cheaper at the same quality.
No. Caching and prompt compression work without any model change. Routing easy requests to a cheaper model is optional, and only used where the eval score holds.
About a week to measure: I read your traces or API logs, add the logging that is missing, and deliver a ranked list of fixes with the expected saving for each. The full engagement runs 1 to 4 weeks with the fixes.
Yes. Caching, routing and compression sit above the provider, so the savings carry over whether you use OpenAI, Anthropic, Gemini, Bedrock or Azure.
I do: the research, the architecture, the UX and the code. You talk to the person who builds your agent, with no hand-offs to a team you never meet.
I started in UI/UX design, became a Solutions Architect on Fortune 500 AI work, including AWS and Azure AI contact centers that lifted CSAT by 40% and halved hold times, then led product as a Principal PM. I founded Workly and now build production AI agents through HireAgentic.
Yes. For larger engagements I bring in pre-vetted engineers from the Ayraxs bench. I stay the lead architect and your single point of contact.
Yes. I explain AI in business terms, focus on the workflow we are changing rather than the framework, and give you a working product you can show users, not a slide deck.
Recent builds cover ERP operations for a Dubai client, a creator-economy SaaS for a US startup, B2B sales outreach and lead qualification. Before that, Fortune 500 work in retail, fintech, healthcare and contact centers.
Yes. As a fractional forward deployed engineer I can join customer calls and implementation work as part of your team, under your brand.
You get the senior person who writes the code, a fixed price agreed before work starts, and full ownership of the code, prompts and evals. Whoever you hire, ask those three questions.
Yes. The case studies show the problem, the architecture, the safeguards and the measured results for each build, including an Odoo ERP agent for a Dubai client and an AI pitch engine for a US startup.