EXPERTS · CUSTOM AI ENGINEERING
Custom AI, built by engineers who ship. Not prompt jockeys.
When Growthzi's built-in AI hits its ceiling, our engineering team ships production LLM apps, RAG on your private data, custom agents, fine-tuned models, and vision systems. Framework-agnostic. Model-agnostic. Honest about when AI is and is not the answer.
A 30-minute discovery call. Feasibility prototype on real data if it makes sense.
WHAT WE BUILD
Six kinds of AI system, all shipped to production.
LLM apps, RAG on your data, autonomous agents, fine-tuned models, vision + speech, evaluations + monitoring. Never a demo you cannot maintain.
LLM apps
Copilot-style chat, generation, summarisation, and structured extraction on top of Claude, GPT, Gemini, or open models. Wrapped in real UX, not a chat box.
Retrieval + RAG
Semantic search over your private docs, contracts, tickets, or product data. Pinecone / Weaviate / pgvector / Qdrant, chunking + reranking tuned for your corpus.
Custom agents
Tool-use agents that call your APIs, browse the web, write to your DB, or run in loops with guardrails. Human-in-loop patterns where they matter.
Fine-tuning
When prompting hits the ceiling on cost, latency, or task-specificity. LoRA + full fine-tunes on Llama, Qwen, Mistral. Evals before + after with your ground truth.
Vision + speech
OCR, layout parsing, visual QA, transcription (Whisper), translation, TTS. Real-time and batch. GPU + on-device deployments.
Evals + monitoring
Every production system ships with an eval suite tied to your ground truth, latency + cost dashboards, and a rollback plan. No black-box demos.
PROCESS · HOW WE DELIVER
From feasibility question to production, without the demo-only trap.
Every AI engagement starts with a feasibility check on your real data. If it does not clear the bar we say so. Numbers land in the brief, not the demo.
- STEP 01 · DISCOVERY
Frame the problem, not just the model.
A 30-minute call to unpack the task, the data, the latency + cost budget, and the eval criterion. If AI is not the right shape we say so on the call.
- STEP 02 · FEASIBILITY
A 2 to 3-week prototype on your real data.
Fixed price. Real data (under NDA). Real evals against ground truth. End of week 3 you get a written go / no-go with the eval numbers, the cost projection, and the production path.
- STEP 03 · BUILD
6 to 12 weeks to production, weekly demos.
Full production system with evals, monitoring, rollback, and deploy pipeline. Slack channel with the engineers writing the code. Weekly demo video every Friday. All code + weights in your infra from day one.
- STEP 04 · SHIP + MONITOR
Rollout runbook, 30 days on-call, retainer optional.
Written rollback runbook. 30 days of post-launch support included. Optional monthly retainer from month 2 for model tuning, re-evals against fresh ground truth, and cost + latency optimisation.
MODELS + TOOLS · FRAMEWORK-AGNOSTIC
The stack that fits your AI system.
Model-agnostic + framework-agnostic. We pick what fits your constraints on cost, latency, privacy, and eval. Not what the AI hype cycle is loudest about this month.
FRONTIER MODELS
Claude · GPT · Gemini · via API or Bedrock / Vertex / Azure
OPEN MODELS
Llama · Qwen · Mistral · DeepSeek · Phi · Gemma · self-hosted
FRAMEWORKS
LangChain · LlamaIndex · FastAPI · Instructor · Pydantic AI
VECTOR + RETRIEVAL
Pinecone · Weaviate · pgvector · Qdrant · Chroma · Elasticsearch
HOSTING + GPU
Modal · RunPod · Replicate · Baseten · vLLM · self-hosted on your cloud
EVAL + OBSERVABILITY
HELM · lm-eval · LangSmith · Braintrust · custom evals on your ground truth
ENGAGEMENT MODELS
How we work together.
Every AI engagement starts with a 30-minute discovery call. If a feasibility prototype makes sense we scope one. If not, we tell you. Numbers land in the brief.
Feasibility prototype
Best for validating an AI idea on your real data.
2 to 3-week fixed-price prototype. Real data (under NDA), real evals, real go / no-go call with numbers. Most engagements start here. You keep the prototype + eval dataset either way.
Production build
Best for a defined AI system heading to prod.
6 to 12-week fixed-price engagement. Full evals, deploy pipeline, monitoring, rollback runbook. Weekly demos. All code + weights in your infra from day one.
Hosting + retainer
Best for ongoing model tuning + monitoring.
Dedicated senior ML engineer, cost + eval dashboards, monthly re-tunes against fresh ground truth. Common after a production build ships. Includes GPU hosting management if you want it.
You keep the discovery call brief, feasibility results, and all technical artifacts even if you never engage further.
AI-SPECIFIC COMPLIANCE
AI systems built for enterprise trust, not demo screenshots.
Every production system ships with the guardrails, monitoring, and documentation the reviewer + auditor actually check.
Data residency + on-prem
Self-hosted open models on your GPUs, on-prem deployment, or region-locked endpoints. Nothing goes to public APIs without your explicit written sign-off.
PII scrubbing + redaction
Automatic PII detection + redaction in prompts, logs, and eval datasets. Configurable per data-class per environment.
Model cards + documentation
Every shipped model gets a written model card: training data, eval results, known limitations, refusal + safety behaviour, rollback plan.
Eval suite + monitoring
Ground-truth eval suite runs on every deploy. Latency + cost + accuracy dashboards. Alerting when eval drift crosses your threshold.
SOC 2 + GDPR-ready
Access controls, audit trail, encrypted storage at rest + in transit. Right-to-delete flows built in for user-facing systems.
Rollback + kill-switch
Every deploy is reversible. Kill-switch flag on every AI feature. Written rollback runbook signed off before go-live.
Common questions from AI discovery calls.
Prompting a public model gets you started; a production AI system needs your data, access controls, evals against ground truth, monitoring, and often a fine-tuned model to be cheap enough at scale. Feasibility prototypes: $10k-$20k. Production systems: $30k-$150k depending on scope. Ongoing hosting + fine-tuning retainers: $500-$5,000 a month, predictable line-item.
Two to three weeks for a prototype on your real data. Real evals, real go/no-go call. Production systems land six to twelve weeks after prototype sign-off, depending on integrations and evaluation depth. Timelines locked in the written brief before code starts.
Yes. Self-hosted open models on your GPUs, on-prem deployment, or region-locked endpoints. Nothing goes to public APIs without your explicit written sign-off. Full source code, model weights (where applicable), deployment scripts, and eval datasets are yours outright. No infrastructure lock-in.
Claude, GPT, Gemini, Llama, Qwen, Mistral, and open vision models. LangChain, LlamaIndex, FastAPI, vLLM, HuggingFace, Modal, RunPod. Vector DBs including Pinecone, Weaviate, pgvector, Qdrant. Framework-agnostic. Fine-tuning wins when tasks are consistent + high-volume, or latency / cost / privacy blocks APIs. Otherwise a good prompt is cheaper and we say so.
Every production system ships with an eval suite tied to your ground truth, a monitoring dashboard for latency + cost, and a rollback plan. We do not ship models we cannot defend with numbers. If AI is not the right fit, we tell you on the first call. Sometimes a rules engine, a search index, or a decent form is cheaper and more reliable. Honest feasibility is why we lead with a free 30-minute discovery call.
READY WHEN YOU ARE
Ship AI you can actually maintain.
A 30-minute discovery call. Bring the task, the data description, and the eval criterion. We give you an honest read on feasibility, scope, and cost. Even if the answer is "AI is the wrong shape here".
You keep the discovery brief + feasibility results even if you never engage further.