AI that reaches production , with the evals to prove it.
RAG, agents, and fine-tuning built by engineers who measure before they promise. If a rules engine solves your problem, we'll tell you in the first call.
Book a technical callModels, clouds, and tools we ship with
From audit to handover
Twelve steps. Each one leaves an artifact in your repo.
Most AI projects die between the demo and the deploy. We invert the order: the evals come before the demo. Every step below leaves something in your repository — a dataset, a benchmark, a documented architecture decision. By step twelve, your team can run the system without us.
Data and systems audit
We map what data you have, where it lives, and what shape it's in. No AI plan survives contact with a dirty database.
Use case and metrics
One use case, and the numbers that prove it works: accuracy, latency, cost per query. What can't be measured doesn't get built.
Model selection
We benchmark candidates — commercial APIs and open weights — on your task; a leaderboard score says nothing about your documents. The right model is the cheapest one that clears your quality bar.
Proof of concept
A narrow build on your real data, in days. Its job is to kill bad assumptions while they're still cheap.
Evaluation suite
A golden dataset from your real cases and an LLM-as-judge calibrated against human judgment. From here on, every change gets scored before it ships.
RAG and integration architecture
Chunking tuned to your documents, hybrid search, reranking, and retrieval evals. Answers with citations back to the source.
Guardrails and security
Prompt injection defenses, PII redaction, output filters, and data isolation, checked against the OWASP LLM Top 10.
Cost and latency
Model routing, prompt caching, trimmed context. Inference-cost reductions of 40–80% are typical industry ranges; we tell you your number after we measure.
Workflow integration
The system connects to your ERP, CRM, help desk, or internal APIs. AI that lives outside your workflows doesn't get used.
Production deployment
CI/CD with eval gates: a change that degrades quality doesn't merge. Deployed in your cloud, in your repository.
Monitoring and continuous evals
Live tracking of quality, cost, latency, and drift. A regression fires an alert before a customer notices.
Knowledge transfer
Documentation, working sessions with your team, and runbooks. 100% of the code has been in your repository since day one; now the understanding is too.
What's included
The deliverables, by name. If it's on this list, it ends up in your repo.
Golden evaluation dataset
Built from your real cases and versioned in your repo. It's the yardstick everything else is measured against.
Calibrated LLM-as-judge
Checked against human labels, with agreement measured. An uncalibrated judge just automates bias.
Regression gates in CI
If a change drops quality below threshold, the pipeline blocks it. Quality stops depending on someone's memory.
Full RAG pipeline
Chunking per document type, hybrid search, reranking, and retrieval metrics like recall@k.
Agents with limits
Explicit tool permissions, token budgets, timeouts, and human-approval steps where the risk demands them.
Prompt injection defenses
Instruction/data separation, input validation, and adversarial testing before deployment.
PII redaction
Personal data is masked before it leaves your perimeter. Configurable per field and per flow.
Data isolation map
An auditable document: which data goes to which model, what gets retained, and what never leaves your infrastructure.
Model routing and fallbacks
Cheap model first, escalation only when needed, and automatic failover if a provider goes down.
Prompt and semantic caching
Repeated queries don't get paid for twice. Lower latency, smaller invoice.
Observability dashboard
Cost per query, latency percentiles, and quality over time. The numbers your CFO and your tech lead will both ask for.
Fine-tuning when justified
Only if the evals justify the cost, with a before/after comparison. Sometimes a better prompt wins.
Architecture decision records
Why each model, index, and threshold was chosen. Written for the engineer who joins in two years.
Incident runbooks
What to do when the model fails, a provider degrades, or costs spike. Step by step, written before anyone needs them at 3 a.m.
Code 100% in your repo
From day one. Your IP, your infrastructure, zero dependence on us to operate.
The six pillars
Every Applied AI system we ship stands on all six. That's what separates a product from a demo.
Every claim has a benchmark behind it. If we haven't measured it, we don't promise it.
OWASP LLM Top 10, guardrails, and PII redaction built into the architecture from the first diagram.
Routing, caching, and trimmed context so per-query economics are predictable before you scale.
RAG with citations to the source, and an explicit refusal when the evidence isn't there.
Defined tools, explicit permissions, and budgets. Autonomy where it helps, supervision where it matters.
Code in your repo, team trained, runbooks delivered. No lock-in.
Validate one use case with evals and real numbers, in weeks.
- One use case, tightly scoped
- Data and systems audit
- Model benchmark against your task
- Golden dataset and evaluation suite
- Working prototype on your real data
- Cost and latency projection at scale
- Go/no-go report with the numbers
- Code in your repo from day one
The complete system: security, monitoring, SLA, and your team trained to run it.
- Everything in the Pilot
- Full RAG or agent architecture
- Integration with ERP, CRM, and internal APIs
- Guardrails: prompt injection, PII, output filters
- OWASP LLM Top 10 hardening and data isolation
- Model routing, caching, and cost optimization
- CI/CD with regression gates and SLA-backed monitoring
- Runbooks, documentation, and knowledge transfer
The questions a CTO actually asks
We map data flows before writing a line of code: what goes to which model, what gets redacted first, and what never leaves your infrastructure. PII is masked before any third-party API call, we configure zero-retention with providers that offer it, and for sensitive workloads we deploy open-weight models inside your cloud. The isolation map is an auditable deliverable you can hand to your compliance team.
It depends on volume, model choice, and context size, so we design the per-query economics before deploying. With model routing and caching, inference-cost reductions of 40–80% versus a naive implementation are typical across the industry. The observability dashboard shows your spend per query from day one.
Against a fixed yardstick: a golden dataset built from your real cases, an LLM-as-judge calibrated against human judgment, and regression gates in CI. You get weekly numbers on quality, cost, and latency, comparable across versions.
Whichever wins the benchmark on your task. We work with commercial APIs like Claude and OpenAI, and with open-weight models when the data can't leave your cloud. Routing picks the cheapest model that clears your quality bar, with automatic failover across providers.
Yes. We integrate with ERP, CRM, e-invoicing, payment gateways, internal APIs, and legacy SQL databases. The initial audit maps exactly those connection points; AI that can't talk to your systems stays a toy.
Four to eight weeks, depending on how ready your data is. The deliverable is a working prototype on real cases, an eval suite, and a go/no-go report with numbers. If the answer is no-go, we've also saved you the rest of the budget.
Retrieval-Augmented Generation: the model answers from your documents, with citations to the source, instead of from its training memory. Done well, it means tuned chunking, reranking, and retrieval evals. Done badly, it's the number-one cause of chatbots that make things up.
Nobody can honestly promise zero hallucinations; we measure the rate and drive it down. Answers grounded in RAG with citations, explicit refusal when retrieval is weak, judge-based checks, and factuality tracked in the evals. The system is built to admit what it doesn't know.
Regression gates catch most failures before deployment. For the rest: fallback across models, degraded mode, escalation to a human where it applies, and monitoring alerts with written runbooks. The Production tier includes an SLA.
When a rules engine, a SQL query, or a better form solves the problem at lower cost and zero uncertainty. If the LLM adds latency and invoice without raising quality, we say so in the audit. Being honest is cheaper for us than maintaining a system that shouldn't exist.
Bring one use case. Leave with numbers.
A 30-minute technical call with an engineer who builds these systems. No slides, no script.
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