Five Production AI Engagements We Deliver

Domain-specific Ask-My-Doc with explicit refusal

We deliver enterprise RAG systems that ground every answer in retrieved evidence and explicitly decline to respond when the retrieved chunks do not support the generation. Clients move from plausible-sounding LLM demos to citation-grade systems that legal, risk, and compliance will sign off on.

Our Delivery Methodology

1. 1

   Phase 1 — Foundations

   Ingest → Chunk (500–800 tokens, 100 overlap) → Embed (Chroma / Weaviate / OpenSearch) → Top-K retrieval. We establish the baseline retrieval pipeline and document store on the client's cloud.

2. 2

   Phase 2 — Production Quality

   Hybrid Search (BM25 + Vector) with Cross-Encoder Reranking and Prompt Versioning. We move clients past naive similarity to citation-grade retrieval that holds up at scale.

3. 3

   Phase 3 — Continuous Assurance

   Curated Golden Datasets (50–200 pairs), offline eval scripts, and CI/CD wired to block PRs whose faithfulness regresses. Quality becomes a release gate, not a quarterly review.

Navigating zero-network environments

For clients constrained by strict privacy regulation, zero-network latency budgets, cost ceilings at massive scale, or offline edge deployment — we deliver locally-runnable, deterministic AI assistants benchmarked against open-weight models and packaged for the client's hardware footprint.

Our Delivery Methodology

1. 1

   Phase 1 — Measurement

   We benchmark Llama 3.2, Qwen, and Mistral 7B (Q4 vs Q5 quantization) on the client's target hardware — documenting tokens/sec and time-to-first-token per model.

2. 2

   Phase 2 — Structure & Determinism

   JSON Schema → Pydantic Validation → Structured Output, with re-prompt on failure. We document output variance between Temperature 0 and 0.7 so engineering teams can pick a setpoint defensibly.

3. 3

   Phase 3 — Deliverable

   A systematic Model Comparison Study using GGUF Q4/Q5 quantization. Typical targets we hit: ~42 T/s throughput, ≥91% human eval quality, under 8 GB RAM.

AI requires Site Reliability Engineering

Knowing WHY a system fails is exponentially more valuable than knowing how to build it. Production AI is 30% building the system and 70% fixing, diagnosing, and scaling it — our observability practice owns that harder 70% for clients already running RAG and agentic systems in production.

Our Delivery Methodology

1. 1

   Phase 1 — Instrumentation

   We trace every step — retrieval, rerank, prompt, tokens — using self-hosted Langfuse or LangSmith. The invisible parts of the pipeline become inspectable.

2. 2

   Phase 2 — SRE Metrics

   We track P50/P95 latency, reject rates, dollar cost per request, citation coverage, and silent-failure rates. Anomalies are detected before end users notice.

3. 3

   Phase 3 — Regression Gating

   Evals are wired into the client's CI/CD. If faithfulness drops, the build fails. We use SmithDB-style storage to support trace searches across 150M+ weekly events.

From Supervised Fine-Tuning to DPO

Fine-tuning isn't for general smarts — it's for consistent excellence on specific, messy tasks. We deliver custom fine-tuned models for clients who need structured JSON extraction from unstructured text, or exact tool-calling and parameter population where careful prompting alone keeps failing.

Our Delivery Methodology

1. 1

   Phase 1 — Supervised Fine-Tuning

   We curate clean data with the client (2k–10k examples) and run LoRA / QLoRA training on A100 or T4. Target metrics: JSON validity rate and exact-match score.

2. 2

   Phase 2 — Preference Tuning

   We generate Output A vs Output B pairs, label Better / Worse with subject-matter experts, and run DPO training to align the model with client-specific preferences.

3. 3

   Phase 3 — Proof of Lift

   We deliver a technical report proving baseline vs post-training metrics. Activation curves on recent engagements have shown ~+80% improvement post-DPO.

Voice assistant architecture under a 1.2 s budget

Batch pipelines are comfortable; live streaming is not. We build voice and multimodal experiences for clients who need to handle messy live data, tight latency budgets, and streaming architectures end-to-end — from audio capture to spoken response.

Our Delivery Methodology

1. 1

   Phase 1 — Pipeline

   We stand up the streaming pipeline end-to-end: Audio Input → ASR (Whisper) → LLM Reasoning → TTS (Cartesia), integrated with the client's existing telephony or app surface.

2. 2

   Phase 2 — Latency Budgeting

   Total response budget: 1.2 seconds, decomposed across ASR time, Network I/O, LLM TTFT, and TTS TTFB. TTFT and TTFB are tracked strictly per release.

3. 3

   Phase 3 — Resilience

   Strict timeout handling with graceful degradation. When a component goes down, the system acknowledges the delay gracefully — it never hangs silently on the caller.