Frequently asked questions.

Most MVPs go live in 3-6 weeks. Full platforms take 6-10 weeks depending on scope. We skip the 6-month discovery phases that agencies love. A 3-day scoping sprint, then we start building in week one. Speed matters, especially in the AI era.

Every engagement is quoted as a fixed scope or a monthly retainer after a discovery call. We avoid hourly billing because it rewards slowness. Starter builds begin around $8k; full platforms typically land between $25k-$120k. You get a written scope before any work starts.

100% yes. Every contract includes a full transfer of source code, design files, and IP on delivery. We ship to your repo, your cloud, your auth. You own everything from day one. No lock-in, no agency-hostage code.

The same senior engineers you talk to in the first call. AutoNex Solution is small by design. No PMs relaying messages, no junior devs learning on your project. The person writing the code is the person replying on Slack.

Yes. A lot of our work is embedding AI into existing codebases. Chatbots grounded in your data, copilots inside your dashboard, predictive features behind existing APIs. We work with your current stack and add the AI layer without forcing a rewrite.

Encrypted in transit and at rest, isolated environments per client, least-privilege access by default. For regulated industries (healthcare, fintech) we sign BAAs and DPAs, follow GDPR practices, and deploy directly on your AWS/GCP/Azure account so your data never leaves your cloud.

Every project includes 30 days of free post-launch iteration: bug fixes, small tweaks, deployment support. After that, ongoing work moves to a retainer or a new scoped project. We don't ghost clients once the invoice clears.

Yes. We're based in Islamabad, Pakistan (UTC+5) and work with founders and teams across the US, UK, EU, and UAE. Our day overlaps a full working morning with the UK and Europe and the evening with the US, so you get same-day replies and weekly live demos, not 12-hour delays. Written scopes, English-fluent communication, and the engineer building your product answering directly on Slack.

At AutoNex Solution, starter AI builds begin around $8k, while full AI platforms typically land between $25k and $120k. Cost is driven by scope, integrations, data readiness, and model complexity, not hours. Every engagement is quoted as a fixed scope or monthly retainer, with a written estimate before any work starts.

RAG (retrieval-augmented generation) is a technique that grounds a large language model in your own data: relevant documents are retrieved from a knowledge base and fed to the model at query time, so answers are accurate, up to date, and cite your sources instead of hallucinating. We use RAG to build chatbots, copilots, and internal assistants on your private content.

An AI chatbot answers questions in a conversation. An AI agent goes further: it plans multi-step tasks and takes actions across your tools (calling APIs, updating records, sending emails) to actually complete work, not just talk about it. AutoNex builds both: grounded chatbots for support and autonomous agents for operations.

It depends on the job. We pick per use case: frontier models (GPT, Claude, Gemini) for the hardest reasoning and quality, smaller or open-source models (Llama, Mistral, Qwen) for cost, speed, privacy, or on-prem deployment. We benchmark candidates against your real data and often route between models to balance quality and cost.

A single-channel RAG chatbot grounded in your documentation typically ships in about 2 weeks. A multi-channel, CRM-integrated business bot lands in 2-4 weeks. We start with a 3-day scoping sprint, then build in week one - no six-month discovery.

We minimise hallucination by grounding every answer in your own data using retrieval-augmented generation (RAG), adding confidence thresholds, and falling back to a human or a safe 'I don't know' when the model isn't sure. We adversarially test for it before launch.

Both. We pick the model per use case: GPT-5 and Claude for most production chatbots, open-source models (Llama, Mistral) when you need on-prem or lower cost. Because we're model-agnostic, we can switch providers without a rewrite.

Yes. We deploy the same brain across web widget, WhatsApp Business API, Slack, Messenger, and SMS, so customers get consistent answers wherever they reach you.

100%. Every engagement includes full transfer of source code and IP on delivery, deployed to your cloud and your accounts. No lock-in, no per-message platform tax.

We agree a target accuracy upfront during the feasibility phase and won't ship below it. Across production projects we typically reach 90%+ on the agreed metric - but the honest answer depends on your data quality, which we assess in the first three days before you commit.

Often, yes. We use pre-trained foundation models, transfer learning, synthetic data, and few-shot prompting to get results with limited labelled data, and we'll tell you in the feasibility phase if the data genuinely isn't there yet.

For most knowledge tasks, retrieval-augmented generation (RAG) is faster, cheaper, and easier to update than fine-tuning. Fine-tuning wins when you need a specific style, format, or task behaviour. We recommend the right one per use case - and frequently combine both.

Yes - a lot of our work is adding an AI layer to an existing codebase without a rewrite: a predictive feature behind your current API, or a copilot inside your dashboard. We work with your stack.

Your cloud, your accounts (AWS, GCP, or Azure). Your data never has to leave your environment, which matters for regulated industries like healthcare and fintech where we sign BAAs and DPAs.

Start with no-code tools (Zapier, Make, n8n) - they're fast and cheap for standard app-to-app flows. Move to custom code when you hit volume limits, need complex logic, want to avoid per-task fees, or need an automation no connector supports. We build both and recommend honestly based on your volume and budget.

It depends on the process, but teams typically reclaim several hours per person per week on the work we automate, and cut error rates close to zero on data entry. We quantify the expected saving during the free process review so you can see the ROI before committing.

Yes. We frequently drop AI steps - classification, extraction, drafting, decisioning - into automations you already run, upgrading them without rebuilding from scratch.

Every workflow ships with error handling, automatic retries, and failure alerts so you know immediately if something needs attention. We also include a monitoring dashboard and 30 days of post-launch support.

Yes. We build in your accounts and hand over full ownership and documentation. There's no dependency on us to keep them running.

Our default is Next.js + TypeScript on the front end, Node or Python on the back end, PostgreSQL for data, and AWS or Vercel for hosting - a modern, well-supported stack that scales and is easy to hire for. We choose per project, not per habit: if your case calls for something else, we'll say so.

A focused MVP goes from brief to production in 4-6 weeks. We skip the six-month discovery phase: a short scoping sprint, then we build in week one with weekly demos so you see progress continuously.

The same senior engineers you meet on the first call. No PMs relaying messages, no junior devs learning on your project. The person writing the code is the person replying on Slack.

Yes - 100% of it, on delivery, in your repo and your cloud. Full transfer of code, design files, and IP is in every contract. No lock-in.

Yes. We audit the current code, document what's there, and pick up development or rescue a stalled build. We'll give you an honest assessment of what to keep and what to refactor.

Both are excellent; the choice depends on your team and product. Flutter gives the most consistent, high-performance UI across platforms from a single codebase. React Native is ideal if you already use React/JavaScript and want to share code with a web app. We build in both and recommend based on your stack and goals.

Yes. With Flutter or React Native we ship both from one codebase with platform parity, which is why most clients get two apps for close to the cost of one. We also do native Swift/Kotlin when a project demands it.

Yes. Store submission, review handling, and listing setup are included, and we deploy under your developer accounts so you own the listings outright.

Absolutely - that's a core strength. We build assistants, computer-vision features, and personalisation directly into mobile apps, with the AI layer designed in from the start rather than retrofitted.

100%. Full source code, store listings, and IP transfer to you on delivery, deployed in your accounts. No lock-in.

A website presents information; a web app lets users perform tasks - log in, work with data, run workflows. If your project needs accounts, a database, real interactivity, or a dashboard, it's a web app, and that's what we build.

Next.js gives us server-side rendering for SEO and speed, a single framework for front and back end, and first-class performance tooling - so we hit 95+ Lighthouse scores by default. It's also widely adopted, which makes your app easy to maintain and hire for.

Yes. We build SaaS front ends and full platforms - multi-tenant architecture, subscriptions with Stripe, auth, and analytics - designed to scale from MVP upward. We can build the whole product or just the web layer on your existing back end.

Yes - every web app we build is responsive and can be made an installable PWA that works offline and feels app-like on phones, often removing the need for a separate native app early on.

100%, on delivery, in your repo and cloud - full code and IP transfer in every contract. No lock-in.

No - modern text-to-speech (ElevenLabs, Deepgram, OpenAI Realtime) is near-indistinguishable from a human, and we tune latency and interruption handling so the agent responds in under a second and lets callers interrupt naturally. Most callers can't tell.

Vapi is the most flexible for custom builds and provider-agnostic orchestration; Bland is best for high-volume outbound; Retell shines for structured, compliance-heavy enterprise flows. We've built and migrated across all of them (including a Bland-to-Vapi migration) and recommend based on your volume, budget, and compliance needs.

Yes. We give the agent tools to check real-time calendar availability, book and reschedule, and read/write your CRM during the call, so a conversation ends with the record already updated.

Both. We build inbound receptionists that answer instantly and outbound agents that run qualification and follow-up campaigns at scale, all logged and measurable.

Usage runs roughly $0.15-$0.35 per minute all-in (telephony + speech + LLM) at moderate complexity, on top of the build. We size this with you during scoping so the ROI is clear before you commit.

The API is the easy 10%. The other 90% - grounding answers in your data with RAG, evaluating and improving quality, guardrailing against hallucination and misuse, controlling cost and latency, and integrating into real workflows - is the engineering that makes it production-grade. That's what we build.

For answering from your knowledge, RAG is usually faster, cheaper, and easier to keep current. Fine-tuning is worth it for a specific style, format, or task behaviour. We frequently combine both and recommend the right mix per use case.

We ground responses in your data with RAG, add evaluation and confidence checks, constrain outputs with guardrails, and design safe fallbacks. We also adversarially test before launch and monitor quality in production.

Yes. We deploy in your cloud (AWS, GCP, Azure) and can use enterprise model endpoints or open-source models so your data never trains a third-party model or leaves your environment.

We're model-agnostic - GPT-5, Claude, and open-source models like Llama and Mistral - and choose per use case on quality, cost, latency, and privacy. Because we abstract the model, switching providers later doesn't mean a rewrite.

Retrieval-augmented generation (RAG) retrieves relevant snippets from your data and feeds them to the model at answer time, so responses are grounded in your truth and can cite sources - instead of the model guessing from its training. It's the single most important technique for making an LLM app accurate, and it's in almost everything we build.

We choose per project: Pinecone for managed scale, Pgvector when you want it inside your existing Postgres, or Weaviate when you need hybrid search. We'll recommend based on your scale, budget, and infra.

Yes - with tool use and function calling, the app can query your database, call your APIs, book, send, or update records as part of a conversation or workflow. That's the difference between a chatbot and an LLM application.

We build an evaluation harness with a labelled test set and automated scoring, so accuracy is a number we track and improve - not a vibe. We agree the target with you before launch and monitor it in production.

No. We abstract the model behind a clean interface, so your app can run on GPT-5, Claude, or open-source models like Llama and Mistral, and switch providers without a rewrite.

A chatbot answers messages; an AI agent pursues a goal autonomously - planning steps, using tools, calling your APIs, and looping until the task is complete. Agents take actions in your systems; chatbots mostly talk. Many of our builds combine both: a conversational front end backed by an agent that does the work.

It is when it's engineered for it. We use strict per-tool permissions, human-in-the-loop checkpoints for high-stakes actions, spending and rate limits, and full tracing so every decision is observable and reversible. We scope exactly what the agent can and can't do upfront.

Instead of one agent doing everything, specialised agents (for example a researcher, a writer, and a checker) collaborate under an orchestrator, each focused on what it's best at. This improves reliability and quality for complex goals - and it's a core part of what we build with LangGraph.

LangGraph is our default for production agents because its graph model gives precise control over state and flow. We also use the OpenAI Agents SDK and AutoGen where they fit. We choose based on reliability, observability, and your stack - not hype.

Yes. We build custom tools that connect the agent to your APIs, databases, and SaaS apps, so it operates inside your real environment - and you own all of it on delivery.