What accuracy can we expect in production?

Document classification and structured extraction consistently land at 96–99% accuracy after iteration. We set targets in scoping and gate releases against them — you'll never wake up to silent regressions.

How do you handle model drift and degradation?

Every system ships with drift monitoring on inputs and outputs. When metrics breach thresholds, the system pages on-call, isolates affected traffic, and routes to fallback paths while we retrain.

Is our data used to train your models?

No. We use customer data only for the specific deployment, never for training shared models. Data residency, retention, and deletion are configured to your compliance requirements.

Can we run models on-prem or in our VPC?

Yes. We deploy self-hosted models (Llama, Mistral) into your VPC or air-gapped environment when data residency or latency demands it. The eval harness and observability come with.

AI Solutions

For teams whose AI pilots never made it out of the notebook

Agentic AI built for production, not demos

Agents, LLM workflows and document intelligence that survive real traffic, audits and edge cases — with a human approving every consequential change.

Scope a project

See the proof

Document intelligence agent

Simulated

Parsing accuracy

98.5%

Requirements traced

61 / 63

Needs human review

Cycle time

10× faster

A human approves every change the AI proposes.

98.5%
Parsing accuracy at Saphira.ai
Saphira.ai case
10×
Faster compliance cycles
Saphira.ai case
60×
Faster regression testing at Qualgent
Qualgent case
8 wk
Platform delivery

Numbers link to the case study they come from. Company-level numbers are across all engagements.

What we build

AI Solutions, in three parts

Mid-market & enterprise teams that need AI that survives production traffic, audits, and edge cases.

01Agentic systems
02LLM workflows
03Decision engines

01Agentic systems AI does this

Agents that execute multi-step work end-to-end

Not chatbots. Agents that plan, call tools, handle exceptions and escalate to a person when confidence drops.

Multi-step reasoningBreak down tasks, orchestrate tools and APIs, run intake-to-decision workflows.
Human-in-the-loopConfidence thresholds, approval queues and audit trails. The agent knows when to ask.

02LLM workflows AI does this

LLM pipelines wired into your systems

RAG, fine-tuned models, structured extraction and multi-modal processing — with monitoring, guardrails and cost controls built in.

RAG & knowledgeAnswers grounded in your documents with citations and hallucination checks.
Structured extractionContracts, records and forms turned into validated, schema-checked data.

03Decision engines AI does this

AI that scores, routes and prioritises at scale

Classification, prioritisation, risk scoring and anomaly detection running on thousands of items an hour — every decision logged.

Autonomous routingScore, classify and route work in real time with a full audit trail.
Predictive signalsDemand, risk, churn and anomaly models that turn data into next actions.

How it runs

Scope in a week, ship in sprints

Every stage produces something you can hand to procurement, audit or your board. Violet is AI-assisted; orange is a human gate.

Stage 01

AI feasibility & data audit

We assess your data quality, label coverage, and use-case fit. Define the eval set, baseline accuracy targets, and the cost-per-decision budget before any model gets trained.

Use-case feasibility
Eval set + baseline
Cost-per-decision budget
Compliance review

run.log · engagement

A human approves every change the AI proposes.

Production-ready APIs. Versioned, rate-limited, OpenAPI-documented endpoints behind your auth and infra.

Evaluation harness. Reproducible eval set, regression tests, and accuracy dashboards we both watch.

Guardrails & safety. PII redaction, output validation, prompt-injection defense, and escalation policies.

Observability suite. Token-cost, latency, accuracy, and drift metrics in your existing tooling (Datadog, Grafana, etc.).

Audit & compliance pack. Model cards, data lineage, and decision logs ready for SOC 2, HIPAA, or insurance regulator review.

Runbook & training. Incident playbooks, retraining procedures, and engineer-to-engineer knowledge transfer.

Stack

Boring, durable tools by default

Cutting-edge only where it earns its keep. What typically ships in this kind of engagement.

Foundation models
OpenAI GPT-4o / 4.1Anthropic Claude Sonnet & OpusGoogle GeminiLlama 3.x (self-hosted)
Orchestration & RAG
LangChainLangGraphLlamaIndexHaystackDSPy
Vector & retrieval
PineconeWeaviatepgvectorQdrantElastic
Eval & observability
LangSmithBraintrustPromptfooPhoenix / ArizeHelicone
Infra & deploy
AWS BedrockAzure OpenAIGCP Vertex AIModalVercel AI SDK

Case study

YC S24Saphira.ai

The compliance platform for safety-critical hardware

Saphira.ai replaces IBM DOORS-era tooling for robotics, automotive and aerospace teams. We built the core platform: AI document parsing, automated risk assessment and full requirement-to-test traceability.

10×

Faster compliance cycles

Read the case

“Quick Automation built our entire compliance platform from the ground up. Their engineers understood the complexity of safety-critical hardware and delivered a system that replaced legacy tools our customers had struggled with for years.”

Akshay Chalana

Co-founder, Saphira.ai (YC S24)

Pairs with

What usually ships alongside AI solutions

Questions

Before you email us

The things buyers ask on the first call.

For most LLM-based use cases (extraction, classification, routing), a few hundred labeled examples is enough to ship. For fine-tuning, we typically need 1k–10k. We assess this in week one and tell you if augmentation is needed.

Scope your AI Solutions project with a senior engineer.

Projects start at $35,000. Typical timeline 6–14 weeks. Reply within 24 hours.

Scope a project

See the case studies

50+
Production systems shipped
97%
Client retention
4–8 wk
Typical time to production
24h
Reply from a senior engineer