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AI is a power grid.
We wire it into
your business.

You don't need to understand how electricity works to turn the lights on. The same goes for AI — we handle the wiring, you run the business.

Start a project See what we build
40+ teams shipped AI features this year
Secure API connection
68% cost reduction
Your AI workflow — live
Customer Support Agent
Handles 80% of tickets automatically
Live ✓
Knowledge Base RAG
Answers from your own docs
Active
Workflow Automation
Invoice → CRM → Notification
Building
Monthly API spend ↓ 41% optimised
Sound familiar?

The blockers holding most teams back

These aren't small concerns — they're the exact questions every product team asks before they get moving.

🗺️
Don't know where to start

Claude or GPT? Fine-tune or RAG? API or no-code? The options are overwhelming and the wrong pick is expensive.

We map the right path for your stack
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Costs spiralling out of control

Token usage adds up fast. A poorly structured prompt loop can turn a $50 budget into a $5,000 surprise bill.

Cost guardrails built from day one
🔒
Security concerns with AI APIs

Sending your data to OpenAI or Anthropic feels risky. What's being stored? Who can see it? Is it training their models?

Data policies reviewed, privacy by design
🎯
Generic chatbots don't cut it

Off-the-shelf AI tools weren't built for your product, your tone, or your customers. They answer anything — and help nobody.

Custom agents tuned to your context
What we build

The things people ask us for, every week

We've heard your exact question before. Here's how we answer it with working software.

Custom AI Agents

Not a chatbot with a personality sticker slapped on it — a proper AI agent that knows your product, escalates when it should, and gets better over time.

Customer Support Sales Assist Internal Ops

Claude & OpenAI API Integration

Already know you want Claude or GPT but not sure how to wire it in? We integrate the API cleanly into your app — with proper error handling, rate limits, and streaming.

Claude API GPT-4o Function Calling

RAG for Your Knowledge Base

Give your AI access to your actual documents — internal wikis, product manuals, support articles — so it answers from your knowledge, not the internet's.

Embeddings Vector DB Semantic Search

Workflow Automation

That process your team does manually five times a day? We map it, model it, and automate it — with AI handling the judgment calls a rule engine can't.

Data Extraction Auto-classification Trigger Chains

AI Cost Optimisation

Prompt caching, model routing, batching, context trimming — there are a dozen ways to cut your API bill without cutting quality. We find them.

Token Reduction Model Routing Caching Strategy

AI Strategy & Architecture

Not sure if AI even fits your problem? We'll tell you the truth. A short discovery call and architecture review can save you six months of wrong turns.

Feasibility Review Stack Advice Roadmap
How it works

From "we have an idea" to live in production

No six-month discovery sprints. No jargon-heavy decks. Just a clear path from problem to working software.

1
Discovery call — 30 minutes

Tell us what you're trying to fix. We'll ask the right questions to understand the workflow, the data, and the real goal — not just the feature request.

2
Architecture proposal

We come back with a clear recommendation: which model, what approach, estimated costs, and realistic timelines. Plain language, no fluff.

3
Build, test, iterate

We build in tight cycles with real feedback. You see working demos — not wireframes — so you can redirect early instead of discovering problems late.

4
Ship and hand over cleanly

Documented, monitored, and handed to your team in a state they can actually maintain. We don't disappear the day we deploy.

Typical project outcomes
Average time to first demo 5–7 days
Support tickets deflected (avg) 72%
API cost after optimisation ↓ 30–60%
Teams who expand scope after v1 8 in 10
Scope clarity at kickoffvs. at launch →
Feature completeness96%
Client satisfaction (CSAT)4.9 / 5
Questions we get every week

Straight answers to real questions

These are the exact things people message us with. We've answered them honestly below.

The starting point is never "which AI model" — it's always "what specific problem are we solving and what data do we have?" Once we understand that, integrating AI is usually a matter of connecting an API to the right part of your product, adding context from your existing data, and making sure the outputs match your UX.
Bithost starts with a free 30-min discovery call to map exactly where AI fits in your stack — before any code is written.

A customer support agent that actually works needs four things: your product knowledge, a clear escalation path, a tone that matches your brand, and a feedback loop so it gets smarter over time. Generic chatbots skip all four. We build agents that know when to answer, when to escalate, and — importantly — when to say "I don't know" rather than hallucinate a wrong answer.
We've built support agents that deflect 60–80% of repetitive tickets while keeping CSAT scores higher than before.

The raw API is straightforward — send a message, get a response. The hard part is everything around it: handling streaming properly, designing prompts that are consistent, managing token costs as you scale, and building retry logic so a failed API call doesn't break your user's experience. We integrate Claude and OpenAI APIs the way they should be — not just "working" but reliable and cost-efficient.
We also help you decide which model is right for each task — Claude Haiku for speed, Sonnet for quality, GPT-4o for multimodal. The choice matters for both cost and output.

Rule-based automation breaks the moment something unexpected happens. AI automation handles the edge cases — reading unstructured emails, classifying documents that don't fit a template, pulling data from inconsistent formats. We map your manual process step by step, identify where AI adds real value, and build something your team can trust to run without babysitting.
Common wins: invoice processing, lead qualification, internal report generation, and multi-step approval workflows.

RAG (Retrieval-Augmented Generation) lets AI answer questions using your actual documents instead of making things up. The setup involves chunking your content, generating embeddings, storing them in a vector database, and retrieving the right chunks at query time. The tricky part is getting the retrieval quality right — bad chunking means the AI finds the wrong passages and gives confident-sounding wrong answers.
We've built RAG systems for internal wikis, product documentation, legal knowledge bases, and HR policy portals — all with accuracy evaluation built in from day one.
Still have questions?

Talk to a real person first.

No sales deck. No follow-up sequence. Just a 30-minute call to figure out if we're the right fit — and to give you genuinely useful advice either way.

Book a free call
Usually responds within a few hours
No commitment required
NDA available on request
30 minutes, no hard sell
Ready when you are

Let's talk about your AI project

Tell us what you're building, what's slowing you down, or what you're not sure about. We'll give you an honest answer — even if that answer is "you might not need us."

sales@bithost.in
Or contact us.