How the systems are actually built.
Written for technical decision-makers. Specific tooling, the reasoning behind each choice, how AI behaviour is evaluated before it is trusted, and a security posture stated without claiming certifications the company does not hold.
- Runtime
- TypeScript · React · Next.js
- Cloud
- Google Cloud or in-country
- AI stack
- LangGraph · Anthropic SDK
- Certification
- None held — see Security
Sources, intelligence, outcomes — and governance across all of it.
The shape every Quilantic system takes. Data is collected from the systems of record and the documents around them, reasoned over, and turned into actions and reporting the operation can use. Governance is not a stage; it applies at every point on the path.
The five layers in detail.
The same architecture as the platform overview, with the reasoning attached.
Data & integration
Getting to the real data, wherever it currently lives.
Connectors into the systems of record already in place, ingestion for the documents and spreadsheets that hold the rest, and pipelines that keep the consolidated view current rather than a snapshot someone exported once.
- System-of-record integrations & APIs
- Document and spreadsheet ingestion
- Scheduled and event-driven pipelines
- Consolidated operational data store
Infrastructure
Where it runs, and who can reach it.
Cloud-native by default on Google Cloud, with in-country deployment available where data residency is a requirement rather than a preference. Separated environments, reproducible builds, and observability from the first deployment rather than after the first incident.
- Google Cloud, or in-country deployment
- Environment separation & reproducible builds
- Observability, logging & alerting
- Access control and secrets management
Intelligence
Reasoning over the operation's own knowledge.
Retrieval across the organisation's corpus, structured extraction from unstructured documents, and agentic workflows that carry multi-step tasks to completion. Every workflow is evaluated against real historical cases before it is allowed to act.
- Retrieval over your document corpus
- Document intelligence & structured extraction
- Agentic workflows — LangGraph, Anthropic SDK
- Evaluation harnesses against real cases
Applications
The surface people actually use.
Internal tools, operational dashboards, portals and approval flows — built for the person doing the job, not for the demo. If the people who own the process will not open it daily, the system underneath has not shipped.
- Internal tools & operational dashboards
- Customer and partner portals
- Approval, exception and review flows
- Reporting people trust enough to act on
Governance
Cutting across every layer above.
Least-privilege access, an audit trail on consequential actions, human approval where judgment or liability sits, and documentation written for the team that inherits the system rather than for the handover meeting.
- Least-privilege access & role separation
- Audit trail on consequential actions
- Human approval at judgment points
- Documentation and source handover
What we build on, and why.
Technology selection is a set of trade-offs, and a firm that cannot articulate the trade-off it made has not made one. These are ours.
Application runtime
TypeScript · React · Next.js
One language across the application surface and its server, a rendering model that keeps pages fast without a bespoke build, and a hiring pool the client can recruit from after handover.
Cloud
Google Cloud, or in-country deployment
GCP by default for managed data services and regional availability. Where residency is a requirement rather than a preference, systems are deployed in-country instead — decided with the client, never silently.
AI orchestration
LangGraph · Anthropic SDK
Multi-step workflows need explicit state, retries and inspectable transitions. A graph makes the control flow legible and testable; a prompt chain does not, which is why chained prompts do not survive production.
Retrieval
Document ingestion with hybrid retrieval
Operational corpora are heterogeneous — procedures, scans, spreadsheets, correspondence. Retrieval combines lexical and semantic matching because operational language contains part numbers and reference codes that embeddings alone handle poorly.
Data
Managed relational stores · scheduled and event-driven pipelines
Operational data is relational and its integrity matters more than its scale. Pipelines run to a schedule or on events, and the freshness of every consolidated view is stated rather than assumed.
Delivery
Reproducible builds · separated environments · observability
Environments are separated from the first commit and monitoring ships with the first deployment. Retrofitting observability after an incident costs more than building it in, every time.
How AI behaviour earns the right to act.
The difference between a demonstration and a production system is what happens on the inputs nobody anticipated. Four practices carry every engagement.
Evaluated against real cases
Before an automated workflow is allowed to act, it is run against historical cases with known outcomes. Accuracy is measured on the client's own data, not quoted from a model card.
Failure modes are designed, not discovered
Every workflow has a defined behaviour for the input it cannot handle — escalate, flag, or halt. Silently producing a plausible wrong answer is the one outcome that is never acceptable.
Humans approve consequential actions
Where a decision carries judgment, cost or liability, a person confirms it. This is an architectural choice made at design time, not a safety net bolted on after a bad result.
Behaviour is observable in production
Automated decisions are logged with their inputs and their reasoning trace, so a disputed output can be reconstructed months later rather than argued about.
What we operate, and what we do not claim.
Quilantic does not hold SOC 2, ISO 27001 or equivalent third-party certification, and does not imply otherwise. The controls below are what the company operates today. Where a client's requirements exceed them, we say so before the engagement starts rather than after.
Data residency
In-country deployment is available where data must remain in Trinidad & Tobago. Where cloud is appropriate, region and provider are chosen explicitly with the client, not by default.
Access & secrets
Least-privilege access, separated environments, and credentials held in managed secret storage rather than in configuration files or shared with the client over email.
Auditability
Consequential actions taken by automated systems are logged and attributable. Where a decision carries judgment or liability, a person approves it — by design, not as a fallback.
Data ownership
Client data is the client's. It is not used to train models, not aggregated across clients, and not retained beyond what the engagement requires.
Reliability
Monitoring, logging and alerting are part of delivery rather than a later phase. Systems are built to be observed, because a system nobody is watching is a system nobody knows has failed.
Documentation & exit
Source and documentation are transferred on delivery. A client who wants to run the system without Quilantic, or move it to another provider, is able to.
If your procurement process requires a security questionnaire, a data-processing agreement or a penetration-test report, ask for it at the outset. We will answer it accurately, including where the answer is “not yet”.
Bring us a hard one.
The most useful technical conversation starts with the constraint that has made this difficult so far — the system with no API, the data that cannot leave the country, the process nobody has fully written down.