AI & Agentic Engineering
AI engineering for enterprises that can't afford to get it wrong.
AI consulting, engineering and operations on AWS — from first use case to a governed fleet of agents in production, delivered by senior engineers who've done it in regulated industries.
What we build
Everything we do in AI.
One team covering the full surface — from strategy and use-case selection to models, agents, data and the platform underneath them.
GenAI applications & copilots
Customer-facing assistants, internal copilots and document intelligence built on Amazon Bedrock and frontier models such as Claude — designed around your data, your tone and your risk appetite.
- Conversational products & support automation
- Document processing & extraction
- Content and code generation workflows
Agentic systems & orchestration
Multi-step agents that take real actions in real systems — with the permissions model, guardrails and human-in-the-loop controls that make that acceptable to your CISO.
- Autonomous & semi-autonomous workflows
- Tool use, function calling & orchestration
- Escalation paths & approval gates
RAG & enterprise knowledge
Retrieval-augmented generation over your documents, wikis and systems of record — grounded answers with citations, access control and freshness guarantees.
- Vector search & hybrid retrieval
- Knowledge-base pipelines & chunking strategy
- Permission-aware answers with citations
Machine learning & predictive analytics
Quantitative and predictive modelling for pricing, risk, demand and customer behaviour — the discipline we built long before it was fashionable to call everything AI.
- Predictive & propensity models
- Real-time scoring & decisioning
- Forecasting & optimisation
Vision, speech & NLP
Computer vision, natural-language processing and speech interfaces for operational workflows — identity checks, claims evidence, quality control, transcription.
- Computer-vision pipelines
- Classification, extraction & summarisation
- Speech-to-insight workflows
AI platform, MLOps & evals
The unglamorous layer that decides whether AI survives contact with production: pipelines, evaluation harnesses, monitoring, cost controls and rollback.
- Eval suites & regression gates for LLM behaviour
- Model deployment, versioning & monitoring
- Token/GPU cost observability & optimisation
Governed by design
How we keep AI safe enough for a regulator — and useful enough for a P&L.
Our compliance heritage is why enterprises trust us with AI. Every system we ship carries its governance with it.
Measured before merged
Behavioural test suites run on every change — accuracy, safety, tone and cost — so regressions are caught in CI, not in the news.
Bounded autonomy
Input and output filtering, PII redaction, allow-listed tools and rate limits. Agents act inside a fence you define.
Every decision reconstructable
Signed, immutable trails of prompts, retrievals, tool calls and approvals — evidence your auditor can actually use.
In the loop where it matters
Approval gates and escalation paths on consequential actions, tuned to your risk matrix rather than a vendor default.
Your data stays yours
Deployed inside your AWS accounts and VPCs in Australian regions. No training on your data. Your IP, your keys.
Responsible-AI frameworks
Practical AI governance policies mapped to your obligations — so the board can say yes with a straight face.
AI on AWS
Native to the platform you already trust.
We build AI the AWS-native way — Bedrock for managed frontier models, SageMaker for custom ML, serverless for the glue — inside the security perimeter you already govern.
Amazon Bedrock
Our Amazon Bedrock consulting covers managed access to Claude and other frontier models, with private networking, guardrails and knowledge bases.
Amazon SageMaker
Custom model training, tuning and hosting with full MLOps lifecycle management.
Serverless inference
Lambda, Step Functions and event-driven patterns that scale to zero and to Melbourne Cup day.
Private by default
VPC isolation, KMS encryption, IAM least privilege and Australian data residency.
Embedded AI engineers
Hire the capability, not just the outcome.
Our senior AI engineers embed with your teams — building alongside your people so the capability stays when the engagement ends. That's how consulting should work: we make ourselves progressively unnecessary.
Squad extension
One or two senior AI engineers inside your existing product teams, shipping from week one.
Full delivery pod
A complete Hatchify pod — engineering, data and platform — delivering an outcome end to end.
Advisory & enablement
Architecture reviews, AI-governance design and hands-on upskilling for your engineers and leaders.
Straight answers
Questions CTOs actually ask us.
We've already run a GenAI pilot that went nowhere. Why would this be different?
Because we start where pilots die: security review, data access, evals and operations. The Flightpath's first two stages exist to surface every blocker — technical, legal and political — before serious money is spent. If the use case can't survive production, we tell you in week two, not month eighteen.
Can our data be used to train models?
No. We deploy inside your AWS accounts, using services with contractual no-training guarantees (such as Amazon Bedrock). Your data stays in your VPCs, in Australian regions, encrypted with your keys.
We're in a regulated industry. Is agentic AI even viable for us?
That's our home ground. Regulated doesn't mean no — it means bounded autonomy: allow-listed actions, approval gates on consequential steps, immutable audit trails and evidence packs mapped to your regulator's expectations. We've delivered under wagering, insurance and financial-services regimes.
Do you resell a product, or build on what we have?
We're engineers, not resellers. We build on your AWS estate, your data platforms and your repos. Everything we produce — code, infrastructure, documentation, eval suites — is yours outright.
How fast can we see something real?
An assessment takes two weeks. A production-grade proof for a well-chosen use case typically lands within the first quarter — deliberately scoped so it clears security and compliance review, because "real" means live, not demoable.
Next step
Bring us your hardest AI problem.
Thirty minutes with a senior AI engineer. We'll tell you what's feasible, what it costs, and what we'd build first.