Question 1

What is AI integration and how does it work?

Accepted Answer

AI integration is the work of building AI capabilities — LLMs, RAG, chatbots, agents, document intelligence — directly into your product, wired into your codebase, data, and workflows so they run in production for real users. It's distinct from using ChatGPT in a separate tab: a properly integrated AI feature is grounded in your data, scoped to a specific use case, governed by validation and guardrails, and observable in production.

Question 2

How long does it take to integrate AI into an existing product?

Accepted Answer

Most first AI features go to production in 4–10 weeks, depending on scope. A focused feature (search, summarisation, a grounded chatbot) on a hosted LLM is typically 4–6 weeks. A broader integration with multiple workflows, retrieval, evaluation, and guardrails is 8–12 weeks. We move fast because we ship this regularly — we built Pakistan's first AI legal chatbot, Lexa, on this same playbook.

Question 3

Which LLM should I use for my product (GPT, Claude, or Gemini)?

Accepted Answer

There's no single right answer — we pick per use case on quality, latency, cost, and data residency. GPT (OpenAI) is strongest for general reasoning and tool use; Claude (Anthropic) often wins on long-context, careful, instruction-following tasks; Gemini (Google) is competitive on price and on multi-modal workloads. Production systems frequently use more than one. We build with a provider-agnostic layer so you're not locked in.

Question 4

How much does AI integration cost in the UK?

Accepted Answer

Costs depend on scope. As a rough range: a focused AI feature on a hosted model is typically £25k–£60k; a deeper integration with RAG, evaluation, and guardrails is £60k–£150k; complex multi-feature builds with custom retrieval and agents run £150k+. Ongoing inference cost is usually a separate small operating line. We scope honestly with clear ranges before any commitment.

Question 5

How do you handle hallucinations and accuracy in production?

Accepted Answer

Five things: grounding (RAG) so the model answers from retrieved facts rather than memory; structured outputs and schema validation so responses can be checked; an evaluation pipeline that tracks accuracy on representative inputs across every change; safe refusal so the system declines when it shouldn't answer; and observability so you see regressions before users do. Accuracy is engineered and measured — not hoped for.

Question 6

Do you sign NDAs before discussing AI projects?

Accepted Answer

Yes. We sign mutual NDAs on request before any detailed discussion of your product, data, or roadmap. Standard practice — it's a paragraph of paperwork that takes minutes, and it makes the conversation more useful for both sides.

Question 7

What's the difference between AI integration and AI consulting?

Accepted Answer

AI consulting is advisory — strategy, opportunity mapping, vendor and architecture recommendations, written assessments. AI integration is the build — the engineering team that takes a chosen direction and ships it to production. Tackxel does both, often together: a discovery engagement that lands a written plan, followed by the team that executes it.

AI Integration Services UK — LLM, Chatbot & AI Feature Development

Fast. Trusted. Autonomous.

Fast

Trusted

Autonomous

Seven ways we build with AI

AI Consulting

AI & ML Development

AI App Development

Chatbot Development

Generative AI

AI Agents

Agentic AI

AI that fits your product, not the other way round.

Built for your product, whatever it does

Your AI essentials, built by senior engineers

Stay true to your brand and your stack

Scale without rebuilding

Five AI capabilities, production-ready.

LLM Integration

Shipped, live, and in real hands.

Pakistan's first AI legal chatbot

AI emotional companion app

Models and tools we ship with

Built for teams who need it to work

Founders adding AI features

Companies who can't hire for it

Teams who want the real thing

Questions, answered straight

Ready to ship AI that actually works?