The Forge

Advanced RAG Systems

Retrieve the right information at the right time, even across millions of heterogenous documents.

• Hybrid dense + sparse retrieval
• Re-ranking & verifiable citations
• Sub-100 ms first-token latency (≤ 500 k chunks, 4 × A10G, batch = 1, no re-rank in hot path)
• Multi-lingual & domain-specific embeddings
• Graph-RAG for relational knowledge
• Continuous index refresh (≤ 15 min SLA)

Targeted Fine-Tuning

Turn a generic foundation model into a domain expert that speaks your brand voice and respects your jargon.

• LoRA / QLoRA, RLHF, DPO
• Brand-voice & domain expertise (≥ 85 % style classifier score)
• Cost-optimised inference: GPTQ 4-bit, AWQ, staging traffic ≤ 50 QPS
• Continual-learning pipelines with rehearsal buffer
• Eval harness: MT-Bench + custom test set (≥ 90 % pass)
• Data requirement: 3 k – 10 k high-quality samples

Agentic Automation

Autonomous agents that orchestrate tools, APIs and legacy systems to complete multi-step workflows.

• Multi-step reasoning & tool use (ReAct / OpenAI functions)
• Human-in-the-loop guardrails (Slack / Teams ≤ 24 h SLA)
• API, RPA, database integrations (OAuth2, SQL, SAP, UiPath)
• Stateful conversation memory (last 20 turns, 32 k ctx)
• Retry & rollback strategies (demo-ready rollback, SQLite WAL, no distributed saga)
• Max 10 tools / workflow, step timeout ≤ 5 s

Safety & Guardrails

Ship with confidence—built-in filters, audits and real-time monitoring keep your model on its best behaviour.

• Content filtering & PII redaction (Presidio, Azure Content Safety)
• Output schema validation (Pydantic / Outlines)
• Toxicity & bias monitoring (weekly review dashboard)
• Prompt-injection detection (heuristic + LLM judge, 90 % precision)
• Audit logs: append-only, 90 d retention, AES-256 encrypted
• SLA: block ≥ 95 % harmful requests

Observability & Cost-Control

Transparent metrics and spend alerts ensure your LLM workload scales without surprises.

• Token-level cost tracking (± 5 % accuracy vs cloud bill)
• Latency & throughput dashboards (Grafana, Prometheus, 7-day retention)
• Auto-scaling inference endpoints (KEDA, 60-120 s cold start)
• Spot-instance fallback layers (stateless pods, checkpoint on EFS/S3)
• Budget alerts: +20 % forecast → Slack + email

Compliance & Enterprise Support

Meet GDPR and basic security requirements with encrypted pipelines and on-prem options.

• VPC & air-gapped deployments (offline registry, HF cache mirror)
• Key-management & encryption at rest (AWS KMS, AES-256)
• Role-based access controls (OIDC, Kubernetes RBAC)
• Static security scan (Trivy) < 3 high-severity CVEs, signed images
• Data residency: EU, US, APAC regions optional

Week 1 · Dev-Ops & Data

• Cloud sandbox with CI/CD (Terraform, GitHub Actions, container build & lint)
• Data pipeline & labelling (≤ 500 k docs, ≤ 1 k tokens/chunk, ≥ 95 % inter-annotator agreement)
• Evaluation harness (hit-rate@5, answer F1, style score, MT-Bench)

Weeks 2-3 · Core Build

• Model training / tuning (8 × A100 40 h, eval delta ≥ +10 %)
• Retrieval stack & APIs (first-token ≤ 100 ms, hybrid RRF fusion)
• Bi-weekly demo & sign-off (recorded Loom + Notion stakeholder sheet)

Weeks 4-X · Hardening

• Load, security & edge-case tests (500 CCU, p95 ≤ 1.2 s, error ≤ 0.5 %)
• Cost & latency optimisation (≤ $0.002 per 1 k input tokens, spot fallback)
• Monitoring & alerting (budget +20 %, SLA dashboard, block ≥ 95 % harmful)

Final · Hand-Over

• Dockerized artifacts (SBOM, < 3 high-severity CVEs, signed images)
• Run-books & docs (rollback, on-call, auto-scale thresholds)
• Knowledge-transfer session (1 × 2 h live + Loom wiki, sign-off sheet)