1) Map the Use-Cases
Workshops to define jobs-to-be-done, user risks, and success metrics. Pick the smallest high-value tasks (assist, summarize, classify, retrieve, generate).
LLM & AI Integration
Go beyond chat. We design and ship AI features:retrieval, summarization, copilots, and automations;backed by evaluation, guardrails, and observability so they’re useful on day one.
Overview
AI features should reduce effort and increase confidence,not add noise. Our pod integrates LLMs where they create real leverage: accelerating workflows, unlocking search across private data, and turning repetitive tasks into reliable automations. We build with measurement, cost control, and privacy top of mind.
Expect pragmatic choices (model selection, latency budgets, caching, fallbacks), transparent UX, and a path to scale across regions and compliance regimes.
Why it matters
- Ship AI that’s accurate, explainable, and traceable.
- Protect data with PII handling, redaction, and access controls.
- Manage latency and cost with smart caching and routing.
- Measure impact with task success, CSAT, and time-to-complete.
Who it’s for
- Teams adding AI to existing products (search, assist, automation).
- New products needing reliable LLM features from day one.
- Orgs with private/regulated data that must remain in-region.
- Global teams with users across Asia, North America, and Europe.
Our Process
2) Design the Pipeline
Retrieval design (indexing, embeddings, chunking), prompt and tool use, grounding sources, and UX patterns for transparency, edits, and citations.
3) Implement & Evaluate
Build the vertical slice with evaluation sets, offline/online tests, cost and latency budgets, and guardrails (schema checks, filters, safety policies).
4) Harden & Observe
Add observability, feedback loops, A/Bs, and escalation paths. Ship with fallbacks, rate limits, and runbooks for on-call confidence.
Patterns & Architecture
Core Capabilities
- Retrieval-Augmented Generation (RAG) & semantic search
- Summarization, classification, extraction, rewriting
- Copilots & workflow automations (tools/functions)
- Multi-step orchestration and memory strategies
Quality & Safety
- Prompt patterns, structured outputs, schema validation
- Reference grounding, citations, and confidence signals
- Evaluation sets, golden tasks, and human-in-the-loop
- PII handling, redaction, RBAC, and regional data controls
Deliverables
- LLM feature spec & UX flows
- Retrieval/data pipeline with index & evaluators
- Prompt kits, guardrails, and fallback logic
- Observability dashboards & cost/latency budgets
- Offline/online evals & success metrics
- Runbooks, handover docs, and training
Global Talent, Unified Vision
We’re remote-first with teams across Asia, North America, and Europe. Distributed by design, we combine local market insight with world-class engineering so your AI features ship quickly, safely, and at global scale.
Ready to add reliable AI to your product?
Share the workflow you want to accelerate: we’ll propose a pipeline, an evaluation plan, and your first sprint.