LLM Integration
LLM integration means building intelligent features into your product or internal tools using models like GPT-4o or Claude — not just wrapping an API and calling it done. We implement retrieval-augmented generation (RAG) so the AI answers questions from your own documents. We build structured output parsing so the AI produces machine-readable data, not just prose. We implement intelligent search that understands intent, not just keywords. Every integration is designed with cost control, accuracy monitoring, and graceful fallback.
At a glance
Estimated cost
$5,000 – $32,000
fixed project price
Typical timeline
6–14 weeks
Deliverables
7
included in standard scope
Cost saving vs West
50–70%
Pakistan-based delivery
What you get
Deliverables
Everything included in a standard engagement. Scope is agreed upfront — no surprises.
- LLM-powered feature integrated into your product or internal tool
- Vector database setup (Pinecone, pgvector, or Supabase vectors)
- Document ingestion and chunking pipeline (for RAG)
- Prompt engineering documentation and version control
- Token cost monitoring and budget alerts
- Accuracy evaluation framework with test cases
- Fallback logic for low-confidence outputs
How it works
Our process
Structured delivery means you know what happens at every stage — before we start.
- 01
Use Case Definition
We define exactly what the LLM needs to do, what data it needs access to, and what constitutes a correct output.
- 02
Data Preparation
We clean, chunk, and embed your knowledge base or documents into a vector store optimised for accurate retrieval.
- 03
Integration Build
We build the retrieval pipeline, prompt templates, and output parsing logic — with structured error handling throughout.
- 04
Evaluation
We run systematic evaluation across representative test cases and iterate on prompts and retrieval configuration.
- 05
Deployment & Cost Monitoring
We deploy with token usage monitoring, budget caps, and alerting configured from day one.
Budget & timing
Investment & timeline
Pakistan-based delivery at a fraction of Western agency rates. Transparent pricing, no retainer traps.
$5,000 — $32,000
per project
Simple LLM feature integration: USD 5,000–10,000. Full RAG system with large knowledge base: USD 15,000–32,000.
6–14 weeks
estimated delivery
Simple integrations: 4–6 weeks. RAG systems over large corpuses: 10–14 weeks.
Tools & technologies
What we build with
We pick the right tool for the job — no forced frameworks.
Who we work with
Industries we serve with this service
Legal
Law firms, barristers' chambers, legal tech startups, and in-house legal teams — modernising document-heavy, process-intensive operations while meeting strict confidentiality requirements.
See how we help →Healthcare
Private clinics, specialist practices, allied health providers, telehealth platforms, and health-tech startups — digitising clinical and administrative workflows while navigating data compliance requirements.
See how we help →Education
Private schools, tutoring companies, online course creators, EdTech startups, and vocational training providers — building and scaling digital learning experiences and administrative systems.
See how we help →E-Commerce
Online retail businesses selling physical or digital products — from single-brand Shopify stores to multi-vendor marketplaces and D2C brands scaling to 7+ figures.
See how we help →Logistics & Supply Chain
Freight forwarders, 3PLs, courier companies, warehouse operators, and supply chain technology providers — managing complex, time-sensitive operations across multiple locations and partners.
See how we help →Real Estate
Property agencies, property management companies, developers, buyers' agents, and PropTech startups — digitising property listings, lead management, and portfolio administration.
See how we help →Who delivers this
Need a dedicated person instead?
AI Engineer
An engineer who builds production AI systems — not demos. LLM integrations, RAG pipelines, classification models, and intelligent automation that runs reliably in the real world.
Hire dedicated →Dedicated Developer
A vetted full-stack, frontend, or backend developer embedded in your team on a dedicated monthly engagement — no agency markup, no context-switching between client projects.
Hire dedicated →Data Analyst
A data analyst who translates messy business data into clear dashboards, automated reports, and the answers your team actually needs to make decisions.
Hire dedicated →Commonly paired with
Related services
AI Automation
Automate repetitive business processes using AI — document processing, lead qualification, customer support triage, data extraction, and workflow triggers.
Custom Software Development
Bespoke software built around your exact workflows — not a SaaS workaround. Internal tools, client portals, automation systems, and multi-role platforms.
Data Analytics
Turn raw business data into decisions — data audits, pipeline setup, predictive models, and the reporting infrastructure that keeps your team informed.
API Integration
Connect your business systems, automate data flows, and eliminate manual data entry. Xero, Stripe, HubSpot, Salesforce, Zapier, and bespoke REST or GraphQL APIs.
Frequently asked questions
Common questions about LLM Integration.
RAG (Retrieval-Augmented Generation) is a pattern where an LLM answers questions by first retrieving relevant content from your own documents or database, then generating a response grounded in that content — rather than relying on its training data alone. You need RAG if you want the AI to answer questions about your specific knowledge base (contracts, manuals, product catalogue, internal policies) accurately and without hallucination.
Accuracy depends on prompt engineering quality, retrieval precision (for RAG), and the inherent complexity of the task. We build evaluation frameworks that measure accuracy systematically — not just qualitatively. Every LLM feature ships with a defined accuracy baseline, and we monitor for drift in production. Features that cannot meet accuracy requirements that matter for your use case are flagged before deployment, not after.
Running costs depend on model choice, token volume, and caching strategy. GPT-4o at USD 0.0025/1K input tokens and USD 0.01/1K output tokens is typical for OpenAI. Claude 3.5 Sonnet is comparable. We configure token cost monitoring and budget alerts from day one, and design prompts to minimise token usage without sacrificing accuracy. For most SMB use cases, monthly API costs run USD 50–500.
OpenAI's API (as opposed to ChatGPT) does not train on your data by default. Anthropic has the same policy. However, we still recommend: (a) not sending PII or sensitive identifiers in prompts — use anonymised IDs, (b) for regulated industries (healthcare, legal), using Azure OpenAI or AWS Bedrock for data residency guarantees, (c) reviewing the API data processing agreements against your compliance requirements. We advise on this as part of every LLM integration scoping.
Ready to start your LLM Integration project?
Send us your requirements. We'll clarify the scope, timeline, and cost — no obligation.