Capability 01
AI systems engineered for production.
Capability 01 / System profile
10 stages
Core disciplines
- Agentic AI
- RAG
- LLM applications
- Model orchestration
- AI evaluation
- Computer vision
Works alongside
01Capabilities
What we engineer.
- 01
Agentic AI
Agents that plan and execute multi-step tasks against enterprise systems through scoped, audited tools. Every action is bounded by permissions, budgets and escalation rules.
- 02
Multi-agent systems
Coordinated agents with distinct roles, such as planner, researcher and reviewer, communicating through explicit contracts. We use them where decomposition measurably improves reliability, not by default.
- 03
RAG
Retrieval-augmented generation that grounds answers in your documents and data, with permission-aware indexing, hybrid search and citations users can verify.
- 04
LLM applications
Language-model features engineered as software: structured outputs, prompt and model versioning, caching, fallbacks and cost controls.
- 05
AI copilots
Assistants embedded in the tools people already use, with context from enterprise systems and clear hand-offs to human decision makers.
- 06
AI automation
Document processing, ticket triage, data extraction and workflow steps automated with confidence thresholds and exception queues for human review.
- 07
Computer vision
Detection, classification, segmentation and OCR pipelines, deployed in the cloud or on edge devices close to the camera.
- 08
Speech AI
Transcription, speaker diarization and voice interfaces, with domain vocabulary and redaction of sensitive content before storage.
- 09
Model orchestration
Routing across commercial and open-weight models by task, latency and cost, with retries, fallbacks and rate-limit handling.
- 10
AI evaluation
Offline test sets, LLM-as-judge and human-graded rubrics, plus online metrics, gating every prompt, model or retrieval change before release.
- 11
AI governance
Model inventories, data lineage, access policy, audit logs and review workflows that make AI usage accountable to risk and compliance teams.
02Reference architecture
From enterprise data to production AI.
Documents, databases, tickets, knowledge bases and event streams, accessed through connectors that preserve source permissions and record lineage.
- Document stores
- Data warehouses
- SaaS connectors
- Event streams
AI system stage
01 / 10
Enterprise Data
Documents, databases, tickets, knowledge bases and event streams, accessed through connectors that preserve source permissions and record lineage.
- Document stores
- Data warehouses
- SaaS connectors
- Event streams
03Concepts
The engineering behind agentic systems.
01Retrieval
How models get the right context.
Embeddings
Numerical vectors that represent the meaning of text, images or code, produced by an embedding model. Semantically similar inputs map to nearby vectors, which is what makes similarity search possible. Model choice, chunk size and dimensionality directly affect retrieval quality and storage cost.
Vector databases
Stores that index embeddings for approximate nearest-neighbour search, typically with HNSW or IVF indexes. Production use also needs metadata filtering, access control and incremental updates. Depending on scale, this can be a dedicated engine or a vector extension such as pgvector in PostgreSQL.
RAG
Retrieval-augmented generation retrieves relevant passages at query time and supplies them to the model as context, so answers are grounded in your data rather than the model's training set. Quality depends on chunking, hybrid search, re-ranking and citation handling. Knowledge stays current without retraining the model.
02Agents
How models act on systems.
Function calling
The model returns a structured request to call a named function, with JSON arguments that conform to a schema you define. Your code, not the model, executes the call and returns the result. Schemas, argument validation and clear error messages determine how reliably the model uses each function.
Tool use
The broader practice of giving an agent a set of functions, such as search, database queries or ticket updates, and letting it decide which to call and in what order. Each tool needs least-privilege credentials, rate limits and an audit trail. A few well-described tools usually outperform many overlapping ones.
Agent memory
State an agent carries beyond a single model call: the working context of the current task, plus longer-term records such as user preferences or prior outcomes. Memory is stored explicitly, scoped per user or tenant and subject to retention rules. Unbounded memory degrades accuracy and creates data-governance risk.
03Control
How systems stay within bounds.
Model routing
Sending each request to the model suited to it, for example a small, fast model for classification and a larger model for complex reasoning. Routing can be rule-based or learned, and includes fallbacks when a provider is slow or unavailable. It is one of the most effective levers on latency and cost.
Human-in-the-loop
Designed points where a person reviews, approves or corrects an AI output before it takes effect. Typical triggers are low confidence, high-impact actions or policy-sensitive content. Reviewer decisions are captured and reused as evaluation and training data.
Guardrails
Deterministic and model-based checks around the model: input screening for prompt injection, output validation against schemas and policy, PII filtering and topic restrictions. They run on every request and fail closed for consequential actions. They complement, and never replace, permission checks inside the tools themselves.
04Quality
How we know it works.
Evaluation
Systematic measurement of AI behaviour against versioned test sets, using exact-match checks, LLM-as-judge scoring and human-graded rubrics. Evaluations run in CI on every change to prompts, models, retrieval or tools. Without them, there is no reliable way to tell whether a change made the system better or worse.
Monitoring
Production telemetry for AI systems: per-request traces, token usage and cost, latency, error rates and quality signals such as user feedback or judge scores on sampled traffic. Alerts fire on drift and regressions. Real failures are fed back into the evaluation set, closing the loop.
04Engineering approach
How we build AI that holds up.
- 01
Evaluation before scale
We define what good looks like and build the test set before the system grows. Every change ships against a measured baseline.
- 02
Least privilege for agents
Agents act through scoped tools with their own credentials, rate limits and audit trails. Permissions are enforced in code, not in prompts.
- 03
Grounded by default
Answers cite retrievable sources and respect the requesting user's access rights. When the system does not know, it says so.
- 04
Humans where it matters
Review and approval steps sit where the cost of an error is high, and reviewer decisions feed back into evaluation.
- 05
Model-agnostic architecture
Models sit behind an internal interface, so they can be swapped or routed as quality, cost and availability change.
- 06
Operable from day one
Tracing, cost tracking and runbooks ship with the first release, not after the first incident.
05Industries
Where we apply AI engineering.
- 01
Healthcare
Retrieval over clinical policy and operational knowledge, with privacy controls and human review on decisions that affect care.
Explore Healthcare
- 02
Financial Services
Assistants and automation for analysts and operations teams, with auditable reasoning traces and strict data boundaries.
Explore Financial Services
- 03
Manufacturing
Computer vision for inspection, and assistants grounded in maintenance and production knowledge.
Explore Manufacturing
06Concept architectures
AI reference architectures.
- Concept Architecture
01Healthcare
AI Clinical Operations
A retrieval-augmented operations assistant that helps clinical operations staff find policy, scheduling and routing information, with human review at every decision point.
View architecture
- Concept Architecture
05Manufacturing
Computer Vision Quality Inspection
Line-side cameras and edge inference that flag surface defects in real time, with operator feedback loops that continuously improve the model.
View architecture
- 06 total
All concept architectures
Illustrative reference architectures across industries and capabilities, each showing how we would approach a hard engineering problem.
Browse case studies
07FAQ
Common questions.
01How do you take an AI prototype to production?
We start by defining success criteria and building an evaluation set from real examples. The prototype is then hardened in stages: data and retrieval pipelines, tool permissions, guardrails, tracing and cost controls, and finally a staged rollout with rollback. Each stage is gated by evaluation results rather than by demos.
02Which AI models do you work with?
We are model-agnostic. We work with commercial model APIs and open-weight models, and select per task based on measured quality, latency, cost and data-residency requirements. Models sit behind an internal interface so they can be changed without rewriting the application.
03How do you stop AI agents from taking unsafe actions?
Agents only act through tools we define, each with least-privilege credentials, input validation, rate limits and audit logging. Consequential actions require explicit approval, and guardrails screen inputs and outputs on every request. Permissions are enforced in code, never only in the prompt.
04How do you measure whether an AI system is working?
Offline, with versioned test sets scored by deterministic checks, LLM-as-judge and human rubrics. Online, with tracing, user feedback, sampled quality scoring and task-level metrics agreed at the start. Regressions block releases.
05Can AI systems run inside our own cloud or private environment?
Yes. When data sensitivity requires it, we design for deployment in your cloud accounts or private infrastructure, including self-hosted open-weight models, private networking to model endpoints and customer-managed encryption keys.
AI & Agentic Engineering
Have an AI system to take to production?
Bring us the use case, the data and the constraints. We'll help you architect a system that can be evaluated, secured and operated.