Eval-driven prompts, RAG with citations, multi-provider routing, observability built in. AI you can trust at 3am — not a demo that breaks under real users.
Whatever shape your AI feature takes, the discipline is the same — eval-driven, traced, multi-provider, with PII handled correctly.
Customer-support, sales-enablement, employee-help-desk chatbots — grounded in your knowledge base, escalating to humans correctly, with full audit trail.
Vector-indexed knowledge bases — accurate retrieval over your docs, contracts, support tickets, internal wiki. Cited sources, no hallucination, evals included.
Multi-step agents that operate within guardrails — schedule meetings, classify emails, run reports, file tickets. Tool-use traces logged for review.
LLM-in-the-loop workflows for ops teams — invoice extraction, contract review, compliance flagging. Human approval gates at every critical step.
Real-time voice agents for support and outbound — Whisper / Deepgram for STT, ElevenLabs / Cartesia for TTS, sub-second turn-taking, transcripts saved.
When prompting isn't enough — supervised fine-tuning, DPO, LoRA adapters. Full eval harness, dataset labeling, drift monitoring after deploy.
Frontier models with multi-provider routing, real evals, real observability — not a thin wrapper around one API.
Every phase has a written deliverable, an eval gate, and a sign-off.
What problem are we solving, what does success look like (deflection %, time saved, accuracy), what's the alternative, what's the cost ceiling. We say no when AI isn't the right tool.
Knowledge sources cleaned, chunked, embedded. PII redaction, access controls, source attribution. Guardrails for hallucination, prompt-injection, off-topic responses.
Iterate prompts against eval set. For agents: tool definitions, JSON-schema validation, retry logic, max-step caps. We commit prompts in version control, like code.
Automated eval suite — golden test set, judge LLMs for soft-eval, regression detection on every prompt / model change. No more "I tried it once and it worked".
Streaming responses, semantic caching, fall-throughs, multi-provider routing (cost / latency tradeoffs), full LangSmith / LangFuse traces, cost dashboards, drift monitors.
Production traces fed back into the eval set. Model upgrades tested in shadow before promotion. Quarterly retraining if you're fine-tuning. Cost optimisation reviews.
12-day SLA on patient queries via email. 6-person team manually handling 800 tickets/week, repetitive responses, no audit. PII a compliance concern.
RAG-grounded support agent over their FAQ + product docs, PII-redacted, human-in-the-loop on medical queries. Deflection 64%, response time 12 days → 8 minutes, audit trail on every reply.
Targets we instrument, alert on, and ship in the production runbook.
Tool-using LLM with grounded retrieval, streaming response, model-agnostic routing. Same shape every project.
1import { streamText, tool } from "ai"2import { anthropic } from "@ai-sdk/anthropic"34export async function POST(req: Request) {5 const { messages }awaitreq.json();67 returnstreamText({8 model: anthropic("claude-opus-4-7"),9 system: SYSTEM_PROMPT,10 messages,11 tools: { search_kb: tool({12 description: "Search BlueSky FAQ + product docs",13 execute: async({ q }) =>vector.search(q)14 }) },15 }).toDataStreamResponse();16}
| Dimension | NextGenUs | Typical AI wrapper shop |
|---|---|---|
| Eval harness from day one | ≥100-case golden set, regression on every change | "It worked when I tried it" |
| Hallucination guardrails | Cited sources, output validators, kill-switch | Fingers crossed |
| Tool-use observability | LangSmith / LangFuse traces · every call audited | console.log() in production |
| Cost & latency control | Multi-provider routing, semantic cache, ceilings | Single GPT-4o, no caching |
| PII / compliance | Redaction, access scoping, audit trail | Sends raw customer data to OpenAI |
| Source code & weights | 100% transferred · prompts versioned in your repo | Locked in their wrapper service |
Build with AI when you have a real problem with measurable success criteria — deflection, accuracy, hours saved — and you're willing to invest in evals. Skip AI if scripted logic, search, or a workflow tool would do it for half the cost. We'll tell you when AI isn't the right answer.
Whether you need a custom web app, a mobile product, or want to explore our HRMS and SFA platforms — let's talk. No commitments. Just a conversation about your goals.