AI Automation Services That Replace Real Work
LLM-powered workflows, RAG systems, and AI agents — production-grade, not demo-ware.
DIRECT LINE — +92 314 7046916
The gap between an LLM demo and a production AI workflow is enormous. Demos work on the happy path. Production systems hit edge cases, hallucinations, latency, cost overruns, and prompt injection. Most teams ship the demo and discover the gap in week two.
We design AI workflows like any other production system: with evals, fallbacks, observability, and a budget. RAG when retrieval is the win. Tools/function-calling when the LLM needs to act. Caching and routing to keep cost predictable. And humans in the loop for anything that touches money or external communication.
- 01LLM workflow design with prompt engineering and evals
- 02RAG pipelines (chunking, embedding, retrieval, reranking)
- 03AI agents with tool use, planning, and guardrails
- 04Document processing: extraction, classification, summarization
- 05Cost monitoring and routing across providers
- 06Prompt versioning + eval harness (Promptfoo or custom)
- 07Human-in-the-loop review for high-stakes outputs
- Anthropic Claude, OpenAI, open-source LLMs
- LangChain, LlamaIndex, custom orchestration
- pgvector, Pinecone, Weaviate
- Promptfoo, Braintrust, custom evals
- Python / TypeScript
- Redis for caching
REAL PROJECTS, ANONYMIZED ON REQUEST.
Most of our work is under NDA. Reach out for a walkthrough of relevant projects in ai automation — we will share scope, architecture, and outcomes for engagements that match yours.
[ REQUEST A WALKTHROUGH ]QUESTIONS WE GET A LOT.
Should I use Claude, GPT, or open-source?
For most production tasks Claude or GPT win on quality-per-token. Open-source is right when you need data residency, fine-tuning at scale, or predictable per-request cost at high volume. We pick based on the workload and run real evals before committing.
RAG or fine-tuning?
RAG when the answer needs current or proprietary context. Fine-tuning when you need a specific output style or format the base model cannot follow reliably. Most production systems are RAG with prompt engineering — fine-tuning is the last 10%.
How do you handle hallucinations?
We design around them: structured outputs validated with Zod or JSON Schema, retrieval-grounded answers with cited sources, evals that catch regressions, and human review for high-stakes outputs. We do not ship workflows where a hallucination causes silent damage.
What does this cost to run?
Production AI workflows typically run $200–$5000/month in inference cost depending on volume. We model the unit economics before building so you know the cost-per-task and where to optimize.