AI agent development / RAG / Connected tools
Useful answers.
Actions with limits.
Your team already has the knowledge. It is spread across documents, systems and people. I build AI assistants and agents that can retrieve approved information, show sources and carry out carefully scoped tasks.
Independent developer · Lahore, working with teams worldwide · 12+ years in WordPress and PHP
Client feedback / Previous WordPress project
he is very easy to work with
Steven J. · 5.00 / 5 · 1 January 2026
Upgrade WordPress / WooCommerce website to Gutenberg
Start with one useful result
A clear scope.
A working handoff.
- A defined user, task and knowledge boundary
- A prototype tested against realistic questions
- Permission rules, handover and a review path
Development, platform subscriptions and ongoing support are scoped separately.
Upwork / Top RatedMuzammil HussainWordPress & WooCommerce engineering.100%Job Success86Jobs2.6KHoursWork with me on UpworkProfile figures checked 2026-10-26. Opens in a new tab.01 / The possibilities
Build around a job worth improving.
01
Customer support assistant
Answer from approved help articles and product information, link to sources and hand uncertain questions to a person. Customer-specific records require the right identity and access checks.
02
Internal knowledge assistant
Help staff find procedures, onboarding material and policies without searching several folders. Retrieval must respect each person’s access to the underlying information.
03
Connected operations agent
Retrieve context, prepare a CRM update, classify an enquiry or draft the next task. Important changes can require review before an authorised tool executes them.
Which job should the agent handle?
Start with one user and one useful outcome. The first version does not need to connect every system.
02 / RAG in plain English
Let the answer start with your sources.
RAG retrieves relevant material before an answer is written.
Retrieval-augmented generation, or RAG, connects a model to selected knowledge. Documents are prepared and indexed. A question retrieves relevant passages, which the model can use to produce a source-backed response.
RAG is not the same as training a new model on your whole business. It also does not remove mistakes. We test retrieval quality, source references and behaviour when the evidence is weak or missing.
The system needs a clear route for “I do not have enough information.” A confident answer without support is not a useful acceptance criterion.
flowchart TD
A["User question and identity"] --> B["Retrieve permitted sources"]
B --> C{"Enough relevant evidence?"}
C -->|Yes| D["Answer with source references"]
C -->|No| E["Ask a question or hand off"]
D --> F{"Action requested?"}
F -->|Yes| G["Permission check and review"]
G --> H["Approved tool action"]Illustrative architecture. A real implementation is tested against your data and permissions.
03 / Choose the implementation
A chatbot is one possible interface.
01
A website or help-centre assistant
A focused chat interface for visitors, connected to public or approved knowledge. Design includes escalation, source links and a clear account of what the assistant can answer.
02
An internal tool or workspace
A staff-facing assistant for searching documents and preparing work. Access rules, document updates and operational ownership are part of the design.
03
An agent inside a workflow
Use n8n or a custom application to combine model reasoning with retrieval and specific tools. Prefer an explicit workflow when its rules are already clear.
The stack can include n8n, custom Node.js or PHP endpoints, a model API, a document pipeline and a suitable retrieval store. We decide whether keyword, vector or hybrid search fits the material. Tool and model choices follow the task, data constraints and operating budget.
External channels such as WhatsApp can be considered when their official APIs, account approvals and usage costs suit the project. Those integrations and costs are agreed in the scope.
04 / A build you can assess
Test the answer and the action.
The delivery path
- Define the user, task, source material and acceptable boundaries.
- Prepare a small, representative knowledge set and prototype.
- Evaluate real questions, missing evidence and access failures.
- Add agreed tools, review steps and an operating guide.
What we review
Quality is more than whether the demo answers one question. We check source relevance, supported answers, refusal and handoff, permissions, document freshness, latency and usage cost.
An agent gets only the tools and access it needs. Retrieved text is treated as data, and sensitive actions need controls outside the model prompt. A pilot establishes what is useful before a wider rollout.
Have documents and a clear use case?
A small evaluation set is a good place to start. You do not need to send private documents with the first enquiry.
05 / Questions
Decide what the agent should own.
Can it use PDFs, website pages or company documents?
Potentially, yes. We first review format, quality, ownership, access rules and how updates are maintained. Scanned files may need extraction work. Not every source belongs in the same knowledge index.
Can an agent take actions, not just answer questions?
Yes, when a defined tool exposes an authorised action. We agree which operations are allowed, how inputs are validated and which actions require a person to approve them.
Will RAG guarantee correct answers?
No. Relevant retrieval can ground an answer in selected material, but both retrieval and generation can fail. Evaluation, source references, evidence thresholds and a human handoff are part of a useful implementation.
Is this a new model trained on my data?
Usually not. RAG retrieves information for the current task rather than automatically retraining model weights. Model training, if appropriate at all, is a separate decision.
Can we start without an autonomous agent?
Yes. A knowledge assistant, a search interface or a fixed workflow may solve the task with less complexity. We can add tool use only where it has a clear purpose.
How are price and ongoing costs handled?
The build is scoped after reviewing the use case, sources and integrations. Model usage, hosting, retrieval storage and ongoing support are identified separately. There is no unlimited-use or accuracy promise.
Where can I read about the approach?
See the RAG overview from Databricks and n8n’s RAG examples for technical context. The proposed implementation is selected for your own requirements.
Your next step
What should your agent do?
Describe the user, the task, the knowledge it needs and any system it should connect to. No confidential documents are needed at this stage.
I review the context, ask about any missing details and suggest a sensible scope. You can decide whether to proceed after that.
No account needed. Please leave passwords, API keys and confidential documents out of this form.
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