Service 02 · AI & automation
Real AI features in production, not a demo.
We've shipped LLM-powered features at SaaS clients ranging from legal-tech to logistics. Eval pipelines, fallback, cost monitoring, prompt versioning — the boring stuff that keeps the magic working.
What's included
What the service consists of.
01
LLM features wired to real workflows
Not a chat widget bolted on. Document review, ticket triage, agent assist, summarisation — embedded in the screens your team already uses.
02
RAG that actually retrieves
Hybrid search, chunking strategies tuned to your content, eval datasets. We don't hand-wave 'pgvector' as the answer.
03
Cost & quality monitoring
Track per-feature spend, response quality (LLM-as-judge + human review), drift over time. Switch models without breaking prod.
04
Multi-provider, multi-model
OpenAI, Anthropic, open-source via Modal or Bedrock. Routing layer so a single outage doesn't kill the feature.
05
Internal ops automation
n8n / Temporal / custom workers for the repeatable manual work eating your team's calendar. Slack-first triggers, audit trail.
06
Privacy-aware design
We help define what data the model sees, redact PII at the boundary, log only what you'd be comfortable showing in court.
How we work
Five steps.
01
Use-case audit
We walk your ops/CS/sales calls and find the 2–3 highest-ROI AI candidates — not what's fashionable, what actually moves a metric.
02
Proof of value
Two weeks: smallest possible end-to-end slice in a staging environment, evaluated on your real data.
03
Productionisation
Eval pipeline, observability, cost caps, A/B vs control group. Same standards as any other production system.
04
Rollout
Gradual: 10% → 50% → 100%. Always with an off switch.
05
Maintenance
Monthly model review (new releases, price drops, perf regressions). Cost-tuning included.
Tools for the job
- OpenAI · Anthropic · Bedrock
- LangChain · LiteLLM · Instructor
- pgvector · Pinecone · Qdrant
- Modal · Replicate · Fireworks
- Temporal · n8n
FAQ
What people usually ask.
Both. Default to OpenAI/Anthropic for prototyping (faster, no infra overhead), evaluate open-source (Llama, Qwen, Mistral) when scale or privacy requires it. Most production systems we ship are hybrid.
We've shipped on pgvector, Pinecone, Qdrant, and plain in-memory FAISS depending on dataset size. We don't recommend Pinecone until you've outgrown pgvector.
Per-feature spend caps, per-user rate limits, automatic fallback to cheaper models on quota breach. Cost regressions tracked weekly.
Yes. We've shipped ops-focused bots for finance, recruiting and legal review. Typical timeline 2–4 weeks.