GitHub Copilot
Autocomplete, boilerplate, and code generation in every PR.
AI-Augmented Development
Your engineers ship. Ours ship faster.
<AI-powered delivery/> for real SaaS roadmaps
Kambda nearshore engineers use GitHub Copilot, Cursor, and Claude Code as standard tools and can build the LLM-powered features your SaaS roadmap now requires. Same time zone. Senior level. Onboard in 10 days.
Cursor
Codebase-aware editing, refactors, and multi-file changes.
Claude Code
Agentic help for multi-step tasks, review, and testing.
LLM Feature Engineering
OpenAI, Anthropic, Gemini. We build AI features into your product.
What we build
Two ways Kambda brings AI to your engineering
AI tooling accelerates how we code. LLM feature engineering puts AI inside your product.
LLM API Integration
Connect your product to OpenAI, Anthropic, or Google Gemini. We handle prompt engineering, context management, token optimization, and API cost control.
OpenAI · Anthropic · Gemini
RAG Pipelines
Retrieval-Augmented Generation over your own data, documentation, knowledge bases, tickets, or product content. Grounded answers, no hand-waving.
Embeddings · Vector DBs
AI Chatbots and Assistants
Conversational AI embedded in your SaaS product for customer support, internal assistants, onboarding flows, and in-app copilots with memory and context.
LangChain · Streaming
Document AI
Intelligent processing of PDFs, contracts, reports, and forms. Extraction, classification, summarization, and structured output ready for your database.
Extraction · OCR
Semantic Search
Replace keyword search with meaning-based retrieval across your product content, data, or users using embeddings and vector similarity.
pgvector · Weaviate · Qdrant
AI-Augmented Code Delivery
Every Kambda engineer ships with Cursor, Copilot, and Claude Code active. Faster test coverage, cleaner PRs, and higher throughput on your existing roadmap.
Cursor · Copilot · Claude Code
Sprint velocity
What AI tooling actually does to developer output
Toggle between a traditional nearshore team and a Kambda AI-augmented team on common engineering tasks.
Real-world applications
What US SaaS teams are actually building
Common LLM feature requests from engineering teams in our ICP and the exact work Kambda engineers handle.
HR Tech / ATS
AI resume screening and scoring
LLM pipeline that parses resumes, scores candidates against job descriptions, and generates structured evaluation summaries for recruiters.
OpenAINode.jspgvector
Customer Success SaaS
AI health score narratives
Automatic generation of plain-language customer health summaries from usage data, support tickets, and NPS inside the CS platform dashboard.
Anthropic ClaudePythonReact
Data / Analytics SaaS
Natural language to SQL
Let non-technical users query data warehouses in plain English. The LLM translates to SQL, validates against schema, and returns formatted results.
LangChainPostgreSQLPython
Fintech SaaS
Document extraction and compliance
Intelligent extraction of structured data from financial documents, contracts, and KYC forms with validation logic and audit trails.
GPT-4oPythonAWS Textract
Product / Dev Tools SaaS
In-app AI copilot
Context-aware assistant embedded in your SaaS UI that helps users complete tasks, explains features, and surfaces relevant actions.
AnthropicReactStreaming
Support SaaS
Ticket triage and auto-response
LLM classifies incoming support tickets, routes them to the right team, drafts response suggestions, and flags escalations.
OpenAIZendesk APINode.js
How we compare
AI-augmented vs. traditional nearshore
What changes when your nearshore team works with AI tooling by default and can build AI features, not just consume them.
| Traditional Nearshore | Kambda AI-Augmented | |
|---|---|---|
| Uses AI coding tools daily | Varies / optional | Standard on every engagement |
| Can build LLM-powered features | Rarely included | Core capability |
| Sprint throughput vs. comparable seniority | Baseline | 2 to 4x higher on common tasks |
| Test coverage on deliverables | Often skipped or minimal | AI-generated test suites as standard |
| Time zone overlap with US | Varies by region | Full CST overlap, year-round |
| Onboarding time | 2 to 6 weeks typical | 5 to 10 business days |
| RAG / vector search experience | Not standard | Available on request |
| Code documentation quality | Inconsistent | AI-assisted inline docs |
The process
From call to first AI-powered commit
Getting started with an AI-augmented Kambda team follows the same engagement model as our standard work, just faster than any US hire.
01
Discovery call
We understand your stack, your AI roadmap, and whether you need AI-augmented velocity, LLM feature engineering, or both.
02
Engineer matching
We surface pre-vetted senior engineers with the right AI tooling experience and tech stack fit. You do the technical screen.
03
You choose, we onboard
You select who joins. Kambda handles contracts, HR, equipment, AI tooling setup, and workspace readiness.
04
First sprint
By day 10 your engineer is in Slack, in standups, and committing code with AI tooling already active.
Contact us
Your roadmap has AI features in it. Your team should too.
Book a 30-minute call with Kambda’s team. We will tell you whether AI-augmented staff augmentation, LLM feature engineering, or both make sense for what you are building.
Contact us
Don’t hesitate to <contact/> us to start discussing your project.
Tell Kambda what you’re building, where delivery is blocked, and what type of engineering support you need next. We’ll route it fast and keep it concrete.
Call us for immediate support:+1-336-666-2253
Request a 30-minute call
Share a few details and the Kambda team will follow up with the right next step.
Common questions
What engineering leaders ask us
Do all Kambda engineers actually use AI coding tools, or is this marketing?
It is standard practice, not a pitch. Kambda engineers use GitHub Copilot, Cursor, and Claude Code on every engagement. Before placement we confirm that a candidate is actively using these tools, not just claiming familiarity.
Can Kambda engineers build LLM features, or just use AI to code faster?
Both. Using Copilot is about how we code. Building RAG pipelines, chatbots, document AI, or semantic search is about what we build. Kambda has engineers with hands-on experience in both.
Does my team need to change how they work to support AI-augmented developers?
No. Kambda engineers integrate into your existing workflow, sprints, review process, and tooling. The AI tooling lives on the developer side. You notice the output, not the process overhead.
What AI models and APIs do your engineers have experience with?
Our engineers have production experience with OpenAI, Anthropic, and Google Gemini, plus Pinecone, Weaviate, Qdrant, pgvector, LangChain, and LlamaIndex where relevant.
How does pricing work for AI-augmented engineers compared to standard staff augmentation?
The engagement model and pricing structure stays the same: $25 to $49 per hour per developer depending on seniority and stack. AI tooling licenses are included in the engagement.