From RAG and agents to fine-tuning and evals. Production-grade systems with p95 < 800ms, evaluation pipelines that run on every commit, and prompts you can version like code — not demo-day theatrics.
Production systems with evals before launch, traces after, and prompts you can git-blame. We build the AI layer, the retrieval layer, the eval layer, and the boring infra under all of it.
Hybrid retrieval (BM25 + dense), hierarchical chunking with tree-sitter for code, citation-required outputs. pgvector when you have Postgres; specialized stores when you don't.
Multi-step orchestration via LangGraph or Temporal, explicit tool grants, human-in-loop on consequential actions. Every step logged for post-hoc audit.
Synthetic data generation, LoRA / QLoRA for parameter-efficient training, hosted on Modal or your own GPUs. We tell you in week 1 whether prompting will be enough.
50–300 cases with you in week 1, pass-rate gates in CI, drift monitoring in production. Output filters for policy, refusal handling for the long tail.
Token-level traces, latency p50/p95/p99 dashboards, cost-per-feature attribution, prompt versioning tied to commits. The runbook ships with the system.
Realtime voice via OpenAI / ElevenLabs, vision-grounded answering, document understanding. We'll tell you when text-only is the right answer.
Production AI has its own rhythm — eval suites before launch, drift monitoring after, prompt versioning in between. Our process is built around real reliability targets, not demo-day theatrics.
Which user task? What's success? What does failure look like, and what's the cost of each kind? Concrete metric targets in writing before any model is touched.
Eval targets · data audit, fixed quote50–300 test cases written with you, baseline model + prompt scored against them, hard cases identified. The eval suite ships before the system ever does.
Eval suite · + scored baselineRetrieval, prompts, fine-tunes — every change scored against the eval set in CI. Weekly demo, weekly written update, weekly pass-rate report.
Weekly demos · + eval reportsPass-rate target met, drift monitor wired, runbook delivered. We don't ship until the suite says so — and the suite keeps watching after.
Live in production · drift alerts + 30-day supportProduction AI systems — each survived its eval suite, made it to production, and stayed there.
A customer-support chatbot grounded in your own help docs via RAG — hybrid retrieval, inline citations, and a human handoff when it isn't sure. 68% of tickets deflected, with zero ungrounded answers.
Upload a client-call recording; Quill transcribes it, extracts scope, timeline and budget, and drafts a proposal with Llama 3.2 — export to PDF or Word in one click.
Drop in invoices, receipts or contracts — OCR + an LLM extract the fields with confidence scores, flag the uncertain ones, and sync the rest to accounting. 98% field accuracy, zero re-keying.
Fixed-price sprints, full builds, or ongoing programs. We'll tell you which fits in the scoping call — and if none fit, who else to talk to.
A fixed two-week burst. Best for prompt audits, eval-suite bootstrapping, or a focused RAG / agent prototype.
Idea to production AI. Full lifecycle — data, evals, build, ship, monitor. Fixed price, eval-gated launch.
Embedded team for ongoing prompts, retraining cycles, and model upgrades. Monthly engagements, roadmap on-call.
A reader, not an accordion. Pick a question on the left — the full answer opens on the right. Filter by topic, or step through with prev / next. Missing one? Ask in the brief and we'll answer in the reply.
Claude Sonnet/Opus for chat and reasoning. GPT-4o when latency dominates. voyage-3 or text-embedding-3-large for retrieval. Llama 3.3 when self-hosted is required.
We benchmark on your task before locking it in — same prompts, same eval set, three candidate models, a written recommendation. The default isn't the choice; the eval is.
Both. ~70% of our builds get 90% there with strong prompting + good retrieval. The other 30% need fine-tuning — usually because the task has tone, format, or domain constraints prompts can't reliably enforce.
We tell you in week 1 which camp you're in. Fine-tuning gets quoted separately because it adds GPU spend and an eval cycle on top.
You do. API keys live in your accounts (Anthropic, OpenAI, etc). Vector DB in your cloud. Training data, weights, and eval sets are your IP. We're vendors on your bill, never on the contract.
Eval suite is the first deliverable, not the last. We write 50–300 test cases with you in week 1, run them on every prompt change in CI, and gate launches on a pass-rate target you sign off on.
After launch the suite keeps running. Drift detector flags when production traces diverge from the eval distribution. Promptfoo / Braintrust by default, custom rig if you need it.
Three layers. Retrieval grounds outputs in your data with required citations. Output filters catch policy violations and out-of-scope responses. Bounded autonomy — agents need explicit permission for anything that touches money, customers, or production systems.
Every response is logged with retrieved sources and tool calls. You can audit any decision the system made in the last 90 days, line by line.
Yes. We've shipped HIPAA-aligned LLM systems with PHI-stripping pre-processing, BAAs across the vendor stack (Anthropic, AWS Bedrock), and SOC 2 type II audit prep.
Regulated work lives in the Program tier. The compliance paperwork alone is real engineering — we won't squeeze it into a Build budget.
All three. Cloud — fastest, cheapest. VPC / private endpoints via Bedrock or Azure OpenAI when data residency matters. Self-hosted Llama 3.3 / Qwen 2.5 on your GPUs when air-gap is non-negotiable.
We ship what your compliance allows. The architecture decision happens in week 0 with your security team in the room.
Sprint tier — $5k, two weeks. We audit your existing prompt, write evals, propose a structured version, run the A/B, ship the winner with rollback plan. Most clients see a 20–40% quality lift on the first sprint.
Real numbers from production AI systems — pulled from LangSmith, Helicone, and provider dashboards (Anthropic, OpenAI). Updated quarterly.
There's a wall of testimonials on the home page. This is the one that matters for AI — a support org that was drowning in tickets and let us deflect 38% of L1 in a single quarter, with zero hallucinated refunds.
We were two months behind on tickets and adding headcount wasn't an option. BytesGenX built a retrieval-grounded copilot in nine weeks, gated launch on a 312-case eval suite, and refused to ship until pass rate cleared 95%.
Three months in: 38% L1 deflection, zero hallucinated refunds, and a Slack channel where my agents argue about which suggestions to upvote. That's the part I didn't expect.
★★★★★"Eval suite first. They wouldn't ship without one. Turns out that's the whole game."
★★★★★"Bounded autonomy with full audit logs. Exactly what compliance asked for. First time IT signed off on an agent."
★★★★★"Production AI, not demo AI. Big difference — one survives Monday morning, the other doesn't."
Whether it's a single prompt audit or a multi-quarter agent build, we reply within 4 hours — usually with a fixed quote, an eval-suite sketch, and a launch-gate target date.
