AI & Machine Learning

Production AI engineering — not demos that die in the notebook.

SimplyRem ships AI and machine learning systems that survive contact with real customers. We build LLM-powered agents, retrieval-augmented generation (RAG) pipelines, fine-tuned models, and classical ML systems for teams who care about evaluation, latency, cost, and the quiet discipline of running AI in production. Every engagement is led by a senior practitioner who has shipped production AI — not just published a tutorial.

What we build

• LLM-powered agents & copilots — multi-step agents on LangGraph, structured tool-use, planning, and memory architectures that don't hallucinate critical fields.
• Retrieval-augmented generation (RAG) pipelines — vector + lexical hybrid retrieval, query rewriting, reranking, and grounded answer generation over your knowledge base, docs, or product catalogue.
• Custom AI integrations — OpenAI, Anthropic Claude, Google Gemini, Mistral, and open-weights models served on Modal, Replicate, or your own GPUs.
• Fine-tuning & distillation — LoRA / QLoRA on Llama, Mistral, and Qwen; distilling expensive frontier-model traces into cheaper production models you can actually afford to run.
• Predictive ML models — churn, fraud, demand forecasting, ranking, and recommendation systems built on PyTorch, XGBoost, and the boring tools that still win.
• AI evaluation & observability platforms — eval harnesses, regression suites, prompt versioning, and the dashboards your team needs to ship LLM features without flying blind.

Why teams choose our AI development services

• Production-first. Every system ships with evals, regression tests, observability, and a written runbook. The notebook is the start, not the deliverable.
• Senior-only delivery. A staff-level ML engineer owns the project from kickoff to launch — most of our team have shipped production AI at startups and large platforms before joining.
• Honest about what AI can and cannot do. We turn down briefs that need more determinism than an LLM can give. You will hear "no" as often as "yes".
• Cost and latency budgets. Token spend, p95 latency, and per-request cost are tracked from day one — not "optimised in phase two".
• Model-agnostic. We pick the right model for the job — frontier when it earns its keep, open-weights when it doesn't. Vendor lock-in is a smell.
• Safety & guardrails built in. Prompt-injection defenses, PII redaction, content filters, and red-team playbooks shipped as part of every customer-facing system.

Frontier model or open-weights — what should you choose?

We are agnostic, and we will tell you honestly. Frontier models (Claude, GPT-4-class, Gemini) are the right call when reasoning quality is the bottleneck, latency budgets are forgiving, and per-call cost is a rounding error against the value created. Open-weights models (Llama, Mistral, Qwen) are the right call when you run millions of calls a day, need on-prem deployment for compliance, or can fine-tune your way to frontier-class quality on a narrow domain. Most of our engagements over the last twelve months have been frontier-model first, with a distilled or fine-tuned open-weights fallback for the tail of high-volume traffic.

Our AI development process

1. Discovery & problem framing (1–2 weeks)

We audit the problem, your data, and your existing systems. We prototype the riskiest assumption — usually retrieval quality or agent reliability — in code, not slides. You leave week two with a written architecture, an eval plan, and a fixed-scope statement of work.

2. Eval-first development (2–4 weeks)

Before the first line of production code, we build the evaluation harness — golden sets, rubric prompts, automated and human-in-the-loop scoring. Every change to the pipeline is measured against the eval. No "it feels better" decisions.

3. Engineering in fortnightly increments

You see real, measurable progress every two weeks — pass rate on the eval set, p95 latency, average cost per call. Not a month-three reveal.

4. Hardening, safety & launch

Red-teaming for prompt injection, jailbreaks, and data exfiltration. PII redaction, content filters, and abuse rate-limiting. A documented runbook for your on-call team — covering model fallbacks, cost spikes, and the things that go wrong at 3 AM.

5. Stewardship

Most clients keep us on a monthly retainer to evolve the system — model upgrades (Claude 3 → 4 → 5 happen fast), prompt regression hunting, eval-set expansion as new failure modes surface, and the slow steady work of pushing answer quality up while cost goes down.

The AI & ML development stack we ship

• Frontier LLMs: Anthropic Claude (Opus / Sonnet / Haiku), OpenAI GPT-4 / o-series, Google Gemini, Mistral Large.
• Open-weights: Llama 3 / 4, Mistral 7B–8x22B, Qwen 2.5, served on Modal, vLLM, or your own H100s.
• Agents & orchestration: LangGraph, LangChain, custom-built when the framework gets in the way.
• RAG & vector search: pgvector, Pinecone, Weaviate, Qdrant, BM25 hybrid, Cohere & Voyage rerankers.
• Fine-tuning & ML: Hugging Face Transformers, PyTorch, Axolotl, Unsloth, Modal for GPU jobs.
• Eval & observability: Braintrust, LangSmith, in-house eval harnesses, Posthog for product analytics on AI features.
• Backend & infra: Python (FastAPI), Node.js, Rust where latency demands it; Kubernetes on AWS or GCP.

Who we work with

Our AI engineering clients are typically Series A–C startups with a senior product or engineering leader, or product teams inside established companies who need an outside studio to ship an AI feature the internal team cannot prioritise. We do our best work when the brief is concrete — a specific user task, a measurable quality bar, a real customer who needs it.

Engagement models & pricing

• Fixed-scope build — $75k–$400k+, two to six months. Best for new AI products with a clear user task.
• AI feature pilot — $40k–$100k, four to eight weeks. Best for bolting a focused AI capability onto an existing product.
• Embedded squad — monthly retainer, two to four engineers. Best for ongoing AI evolution alongside your internal team.
• Discovery sprint — $25k, two weeks. A de-risking exercise that produces an eval set, an architecture, and a fixed quote.

We are booking new AI engagements one quarter at a time. Tell us what you are building — we respond to every brief within one business day.