AI Engineering Services for Startups & Scale-Ups
We work with companies at every stage — from zero-to-one MVPs to scaling production systems. Every engagement is scoped for real impact, not billing hours.
Custom AI Development
We design and build end-to-end AI systems tailored to your workflows from LLM pipelines and voice AI to automation tools.
Learn more →from langchain import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
def build_rag_pipeline(docs, llm):
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(
docs, embeddings
)
retriever = vectorstore.as_retriever(
search_kwargs={"k": 4}
)
return RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)AI Strategy and Consulting
We identify where AI actually creates leverage in your business, define the right architecture, and give you a clear execution roadmap — no vague recommendations.
Learn more →roadmap:
phase_1: Discovery & Audit
duration: 2 weeks
deliverables:
- Use case prioritisation matrix
- Data readiness assessment
- ROI projection per use case
phase_2: Architecture Design
duration: 1 week
deliverables:
- System design document
- Model selection rationale
- Execution blueprintAI Infrastructure & LLMOps
We set up the backbone for reliable AI systems — deployment pipelines, monitoring, evals, scaling, and cost control — so your models run smoothly in production.
Learn more →resource "aws_eks_cluster" "ai_cluster" {
name = "prodinit-prod"
version = "1.29"
vpc_config {
subnet_ids = var.private_subnets
}
}
resource "helm_release" "model_server" {
name = "vllm"
repository = "oci://prodinit/charts"
chart = "model-server"
set {
name = "replicas"
value = 3
}
}Model Finetuning & Optimization
We improve model performance for your specific use case through fine-tuning, prompt engineering, RAG optimization, and evaluation frameworks.
Learn more →from openai import OpenAI
client = OpenAI()
job = client.fine_tuning.jobs.create(
training_file="file-abc123",
model="gpt-4o-mini-2024-07-18",
hyperparameters={
"n_epochs": 4,
"batch_size": 16,
"learning_rate_multiplier": 0.1
}
)
# Result: +35% persona accuracy
# vs base GPT-4 on eval setStay ahead in AI engineering.
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