Questions, Answered

We build four main categories of AI systems. First, AI agents and workflow automation: software that handles repetitive business processes like lead follow-up, data entry, reporting, and scheduling without human involvement. Second, voice AI agents: AI that answers and makes real phone calls, books appointments, and qualifies leads. Third, custom chatbots and LLM applications: assistants grounded in your business data through RAG pipelines, plus AI features embedded inside existing products. Fourth, AI-powered products built from scratch, where the AI is part of the product itself rather than an add-on. We build on OpenAI, Anthropic, Google Gemini, and open-source models, and we pick the model that fits your use case and budget rather than defaulting to one vendor.

Almost always yes, and usually starting smaller than people expect. AI does not require enterprise budgets anymore. The best first project is a single painful workflow: missed phone calls, leads that go cold before anyone follows up, hours spent copying data between systems, or weekly reports someone assembles by hand. We scope a focused automation around that one workflow, prove the return in weeks rather than months, and expand from there. Some of the highest-ROI systems we build are for teams of five to fifty people, because that is where one automated workflow replaces a meaningful share of someone’s day.

Three signals. First, volume: the task happens daily or many times a day. Second, structure: the task follows rules a person could write down, even messy ones. Third, cost of delay or error: leads cool off, invoices slip, data gets mistyped. If a workflow hits two of the three, it is probably worth automating. Our free consultation includes an AI opportunity audit where we map your workflows against these criteria and rank them by expected return, so you start with the automation that pays for itself fastest.

This is the right question to ask, and the honest answer is that unmanaged AI does make things up. Managed AI, built properly, rarely does, and fails safely when it might. We control this in four ways: grounding responses in your actual data through retrieval (RAG) so the AI cites sources rather than inventing them, constraining what the AI is allowed to say and do through guardrails, testing the system against evaluation sets before launch and continuously after, and building escalation paths so anything uncertain routes to a human instead of guessing. No serious vendor will promise zero errors. What we promise is measured accuracy, visible logging, and a system that says "let me connect you with the team" instead of making something up.

It hands off. Every agent we build has explicit escalation logic: to a human via live transfer, callback, ticket, or notification, depending on the channel. The agent logs the full context of the conversation so the human picks up where the AI left off rather than starting over. We also review handoff logs during the first weeks after launch, because they show exactly where the agent needs to improve. A well-built system’s handoff rate drops steadily over time.

We work across OpenAI (GPT models), Anthropic (Claude), Google (Gemini), and open-source models where they fit. Model choice depends on the task: some models are better at conversation, others at structured extraction, others win purely on cost at volume. We architect systems so the model layer is swappable, because models improve monthly and pricing shifts constantly. You are never locked into a vendor through our code, and when a better or cheaper model appears for your use case, switching is a configuration change, not a rebuild.

No, not when configured correctly, and we configure it correctly. The major AI providers offer API terms under which your data is not used for model training. We build on those terms, and where a client needs stricter guarantees we can use enterprise agreements, regional data residency options, or self-hosted open-source models so data never leaves your infrastructure. We will walk you through exactly where your data flows before anything ships, and we are happy to put data handling commitments in the contract.

A chatbot answers questions. An agent does work. A chatbot on your website can tell a customer your opening hours; an agent can check the calendar, book the appointment, send the confirmation, update the CRM, and set a reminder follow-up. Agents connect to your tools and take actions with appropriate permissions and logging. Most businesses that ask for a chatbot actually need an agent, so ask any vendor what actions their system can take, not just what questions it can answer.

ChatGPT is a tool a person uses. What we build is infrastructure that works without a person. The difference shows up in three places: integration (our systems connect to your CRM, phone lines, calendars, and databases; ChatGPT does not know your business exists), reliability (we add retrieval, guardrails, evals, and monitoring so the system behaves consistently at 3 a.m. on a Sunday), and autonomy (our agents act on triggers like a new lead or an inbound call rather than waiting for someone to type a prompt). Teams using ChatGPT well are our favorite clients, because they already understand what the technology can do. We take it from "helpful tool someone uses" to "system the business runs on."

We automate what you have. Most automation projects connect existing tools rather than replacing them: your CRM (we work extensively with HubSpot, Salesforce, GoHighLevel, and others), email and calendars, spreadsheets, accounting software, phone systems, and internal databases. If a tool has an API we can integrate it, and for legacy systems without APIs there are usually still workable paths. Replacing software is a last resort we will only recommend when the existing tool is genuinely the bottleneck.

Modern voice AI is remarkably natural: sub-second responses, human-sounding voices, and the ability to handle interruptions and topic changes. Callers regularly complete entire calls without realizing they spoke to an AI. That said, we recommend the agent identify itself as an AI assistant, both because it is honest and because in some jurisdictions it is legally required. In practice, callers care far more about getting their problem solved immediately than about whether the voice is human. An AI that answers on the first ring at 9 p.m. beats a voicemail box every time.

A well-built voice agent can answer questions about your business, qualify a caller against your criteria, check real calendar availability and book appointments, take detailed messages, collect and verify caller information, send SMS confirmations and follow-ups during or after the call, log everything to your CRM, and transfer to a human with full context when needed. On the outbound side: appointment reminders, follow-up calls to leads, reactivation campaigns to past customers, and confirmation calls. What it should not do is handle situations requiring genuine judgment or sensitive conversations; those route to your team.

Yes. We integrate with your existing phone system through SIP or by porting/forwarding, so callers dial the same number they always have. Common setups include: AI answers everything and transfers when needed, AI answers only after-hours and overflow, or AI handles a dedicated booking line while the main line stays human. We will recommend a setup based on your call volume and what the calls are for.

It transfers gracefully. The agent is built with confidence thresholds: when a caller’s request falls outside what it can reliably handle, it says so plainly and routes to a human, a callback queue, or a message, depending on your setup and the time of day. The full transcript and context travel with the handoff. We also monitor calls (with appropriate consent and disclosure) during the tuning period after launch and iterate on the scenarios that trip it up. Confusion cases are training data, and they drop sharply in the first few weeks.

Any business where the phone is a revenue channel and calls get missed: dental and medical clinics, med spas, home services (HVAC, plumbing, roofing), law firms, real estate teams, salons, and agencies managing calls for clients. The math is simple: if a booked appointment is worth $200 and you miss five calls a week, that is roughly $50,000 a year leaking through the phone line. A voice agent that recovers even half of that pays for itself many times over.

Absolutely. Full-stack web and mobile development is where we started and remains core to what we do: SaaS platforms, e-commerce, dashboards, APIs, and iOS/Android apps built with React, Next.js, Node.js, and React Native. Not every product needs AI, and we will tell you when yours does not. That said, many projects now combine both: a product built from scratch with AI features designed in from day one, which is far cleaner than bolting them on later.

Yes, and it is a large share of our work. We start with a technical audit: architecture, code quality, security posture, dependency health, and deployment setup. Then we give you an honest read on what to keep, refactor, or rebuild, with the trade-offs priced out. We are comfortable inheriting messy codebases; almost every codebase with real users is messy somewhere. What matters is stabilizing it first, then improving it without breaking what works. Adding AI capabilities to an existing product is one of the most common versions of this engagement.

AI and LLMs: OpenAI, Anthropic Claude, Google Gemini, LangChain, RAG pipelines, vector databases like Pinecone and pgvector. Automation: n8n, Make, Zapier, and custom orchestration engines, with deep CRM integration experience across HubSpot, Salesforce, and GoHighLevel. Voice: real-time voice pipelines, telephony and SIP integration, speech-to-text and text-to-speech. Web and mobile: React, Next.js, Node.js, TypeScript, React Native, Electron. Data and infrastructure: PostgreSQL, MongoDB, Redis, AWS, Docker, CI/CD pipelines. Blockchain where needed: Solidity, Ethereum, smart contracts.

That is an architecture decision made on day one, and we make it deliberately. We build on cloud infrastructure with horizontal scaling paths, use databases and caching appropriate to your growth curve, and design AI systems with cost-per-request in mind so success does not bankrupt you on API bills. We also avoid the opposite mistake: over-engineering an MVP for a million users it does not have yet. The right approach is a clean architecture that scales in stages, and we will show you what each stage looks like before you commit to any of them.

It ranges widely, so here is honest orientation rather than a single number. A focused automation (one workflow, one or two integrations) is the entry point. A voice AI agent with telephony, calendar, and CRM integration sits in the mid-range. Custom chatbots grounded in your data land similarly depending on data complexity. Full products with AI built in are scoped like any custom software build. Two things keep costs predictable: we quote fixed-scope proposals so you know the number before work starts, and we deliberately structure first projects small so you can validate ROI before committing to anything larger. The free consultation ends with a real number for your specific case.

A focused automation: two to four weeks. A voice AI agent: three to six weeks including telephony setup and a tuning period. A custom chatbot with RAG: three to six weeks depending on data sources. An MVP web or mobile product: eight to fourteen weeks. Enterprise platforms: scoped individually with milestone-based delivery. AI projects include a tuning window after launch where we refine behavior against real usage, so budget one to two weeks of iteration into any AI timeline.

Three. Fixed-scope projects: we define deliverables together, quote a fixed price, and deliver against milestones; best for well-defined builds. Retainers: a monthly engagement for ongoing development, automation expansion, and maintenance; best after an initial project proves out and you want to keep improving. Staff augmentation: our engineers embed in your team on a monthly basis under your direction; best when you have technical leadership and need capacity. Many clients start with a fixed-scope project and move to a retainer once the first system is live.

Yes, and for AI systems specifically we recommend it. Traditional software mostly keeps working once shipped; AI systems live in a shifting environment where models get updated, APIs change, and your business data evolves. Our maintenance covers monitoring and alerting, model and dependency updates, accuracy checks against evaluation sets, and a monthly improvement budget for refinements. It is optional: everything we hand over is documented well enough for your own team to run. But most clients keep at least a light retainer for the AI components.

A real working session, not a sales pitch. We spend 30 to 60 minutes understanding your business and workflows, identify where AI or automation would produce measurable return (and where it would not), and follow up with a short written summary: recommended first project, expected outcomes, timeline, and a fixed price. If the honest answer is that you do not need us, or that an off-the-shelf tool would serve you better, we will say exactly that.

Anything from a single automation or focused MVP to a full enterprise platform, and we have delivered 250+ projects across that whole range. The floor is roughly a project big enough to scope properly: if your need is genuinely tiny, we will point you to the off-the-shelf tool that solves it instead of quoting custom work you don’t need. The ceiling is set by complexity rather than size; we staff multi-team engagements with dedicated leads. If we are not the right fit, we will tell you honestly.

Three steps. First, we reply within one business day to schedule a call at your convenience, usually within the same week. Second, we run a discovery session to understand your goals, workflows, and constraints; for AI projects this includes an opportunity audit of where automation would pay off fastest. Third, we send a scoped proposal: recommended approach, deliverables, timeline, engagement model, and a fixed price. No obligation at any step, and no endless sales sequence if you decide not to proceed.

You get a dedicated project manager and direct access to the engineering team; we do not hide engineers behind account managers. Standard setup: a shared channel (Slack, WhatsApp, or your preference) for day-to-day, a weekly progress call, and demo checkpoints at every milestone so you see working software rather than status decks. We work with clients worldwide and schedule communication in your timezone. You will never wonder what state your project is in.

Openly and with a process, because requirements always change, and in AI projects they change even more once people see what is possible. Small adjustments get absorbed into the sprint. Meaningful scope changes get written up with their impact on timeline and cost before we act, so you decide with full information and there is never a surprise invoice. Our fixed-scope proposals include this change process explicitly.

More rigorously than most, because AI failures are subtler than crashes. Alongside standard QA (code review, automated tests, staging environments), AI components get evaluation sets: dozens to hundreds of realistic test scenarios the system must handle correctly, including deliberately adversarial ones (confusing questions, off-topic requests, attempts to manipulate the agent). Voice agents additionally go through scripted call testing and a supervised soft-launch period. We define accuracy targets with you before launch and show you the measurements, not just a thumbs-up.

Less than you might fear, but a few things matter: access to the tools we are integrating (CRM, calendars, phone system) with appropriate permissions, the business knowledge the AI needs (your FAQs, policies, pricing, qualification criteria; usually gathered in one or two working sessions), a point of contact who can make decisions within a day or two, and honest feedback during the tuning period. We handle everything technical. The projects that go fastest are the ones where the client-side contact is responsive.

Yes to the NDA: we are happy to sign a mutual NDA before discussing anything sensitive. And you own everything: code, architecture, prompts, agent configurations, documentation, and any accounts created for the project. No vendor lock-in, no licensing fees on your own product, no per-seat charges on systems we build for you. If you leave us for another vendor tomorrow, you take a complete, documented, runnable system with you. We put this in writing in every contract.

As a default, not an add-on. Concretely: secrets management and least-privilege access on every integration, encrypted data in transit and at rest, dependency auditing and supply-chain hygiene, environment separation between development and production, and audit logging on agent actions so there is always a record of what an AI system did and why. For regulated verticals like healthcare and fintech we scope compliance requirements (data residency, access controls, retention policies) into the project from day one rather than retrofitting them.

DevenCodes is based in Lahore, Pakistan, and works with clients worldwide, with most engagements running remote-first. We schedule calls and communication in your timezone and structure overlap hours so you always have a live window with the team each working day. For clients in the US and UK this often works in your favor: work you request at the end of your day is frequently done when you wake up.

Three honest reasons. Cost-to-quality: you get senior engineering at rates that make ambitious projects viable, without the quality drop-off people fear from offshore work; our portfolio and client relationships (10+ years with some clients) are the evidence. Specialization: we have deep, current AI and automation capability that many local generalist agencies are still building. Process: fixed scopes, published salaries, real named team members with GitHub profiles, full code ownership, and communication in your timezone remove the classic offshore risks. And if your project legally requires onshore data handling or in-person collaboration, we will tell you that in the first call.

Yes. After an initial conversation confirms we are a plausible fit, we can connect you with references relevant to your project type, and several client testimonials with names and companies are on the site already. We would rather you hear "they replaced our manual reporting and the team finally trusts the numbers" from the client who lived it than from our marketing copy.

Most "AI agencies" in 2026 are one of two things: traditional dev shops that added AI to the menu last quarter, or automation resellers who assemble no-code templates and cannot go deeper when the template runs out. We are engineers first: we build and operate real-time voice infrastructure, custom agent orchestration, and full-stack products, which means when your project outgrows the no-code layer we keep building instead of hitting a wall. And we are honest about fit: we will tell you when a cheap off-the-shelf tool solves your problem, because clients we save money for come back with bigger projects.