AI & IT Solutions
Put language models behind real work, with evidence they are safe to trust.
Seaggle designs and builds retrieval systems and multi-agent workflows against your own data and systems, with the evaluation harness and controls that decide whether an answer is good enough to act on.
When this fits
Signals that this is the right conversation
Most AI programmes do not fail on model quality. They fail because retrieval returns the wrong context, because nobody defined what a correct answer looks like, because permissions were handled in a prompt rather than at the data layer, or because there was no way to tell whether last week's change made the system better or worse. Those are engineering problems, and they are the ones we solve.
- A pilot answered well in a demo and stalled when it met real documents and real users
- Answers are plausible but unverifiable, so nobody will let the system act unattended
- Knowledge is spread across systems with different permission models
- You need an agent to take actions, not just produce text, and the risk is unbounded
- You cannot currently answer the question "is this getting better or worse?"
What Seaggle delivers
Deliverables, arranged by when they arrive
Each of these is an artifact you receive and can use without us in the room.
Assess
- Use-case scoring on value, data readiness, and failure cost
- Corpus audit — coverage, freshness, duplication, and permission boundaries
- Baseline evaluation set drawn from real queries, labelled with subject experts
- Architecture decision record: retrieval vs fine-tuning vs both, with the reasoning
Build
- Retrieval pipeline — chunking strategy, embeddings, hybrid dense + keyword search, reranking
- Permission-aware retrieval enforced at query time, never in the prompt
- Grounded generation with citations, abstention, and confidence routing
- Agentic workflows with scoped tools, typed state, retries, and human approval gates
Operate
- Evaluation harness wired into CI so a prompt or model change is a measured decision
- Tracing across every step, with token, cost, and latency budgets enforced
- Online quality sampling, drift alerts, and a rollback path
- Runbooks and handover so your engineers own the system
Solution patterns
Shapes this work commonly takes
These are patterns rather than products. Select one to see how it works, where a person stays in the loop, and what we design against.
Retrieval-augmented assistant (RAG)
People cannot reliably find current, correct information spread across wikis, tickets, contracts, and file shares.
- How it works
- Query is rewritten and expanded, candidates are retrieved by hybrid dense and keyword search under the asker's own permissions, a cross-encoder reranks them, and the answer is generated strictly from retrieved context with citations back to source.
- Human checkpoint
- Citations are shown inline; low-confidence or low-coverage queries abstain and route to a person rather than guessing.
- Risks we design against
- Stale or contradictory sources, over-broad permissions, and confident answers where the corpus is genuinely silent. Each is handled explicitly — freshness policy, query-time authorisation, and a measured abstention threshold.
Multi-agent operational workflow
A process needs several steps, tools, and judgement calls that a single prompt cannot hold.
- How it works
- A supervising graph decomposes the task, routes each step to a specialised agent with a narrow toolset, keeps typed state between steps, and compensates or retries on failure. Consequential actions pause for approval.
- Human checkpoint
- Approval gates on anything that writes to a system of record, spends money, or contacts a customer.
- Risks we design against
- Unbounded loops, runaway cost, and silent partial failure. Controlled with step caps, token and spend budgets, deterministic tool contracts, and full execution traces.
Structured extraction at volume
Documents arrive continuously and are read, classified, and re-keyed by hand.
- How it works
- Documents are parsed with layout awareness, fields are extracted against a schema with per-field confidence, validated against business rules, and either committed automatically or queued for review.
- Human checkpoint
- Confidence thresholds per field decide what commits automatically and what a person checks.
- Risks we design against
- Silent format drift and over-trusted extraction. Handled with schema validation, per-field confidence, and a review queue that feeds corrections back into evaluation.
Representative workflow
End to end, with the checkpoints visible
A worked example of how the pieces connect in production. The static sequence below is the whole content — nothing is hidden behind animation.
- 01
Request enters
From a channel, queue, or interface, with the requester's identity attached.
- 02
Context and permissions resolved
Authorisation applied at retrieval time so the model can only see what the requester can see.
- 03
Relevant knowledge retrieved
Hybrid search, then reranking, then assembly into a context window with provenance kept.
- 04
Response drafted with citations
Generated only from retrieved context, with a confidence signal and sources attached.
- 05
Person approves or corrects
Nothing consequential is automatic; corrections are captured as evaluation data.
- 06
Action committed and logged
The approved action updates the system of record with an audit entry.
- 07
Outcome evaluated
Sampled into the evaluation set so the next change is measured, not guessed.
Representative example
Evaluation & controls
What separates production work from a demonstration
A demonstration proves something can happen once. These controls are how you know it keeps happening correctly.
Grounding and abstention
Answers are generated from retrieved context with citations. Where coverage or confidence is insufficient, the system declines and routes to a person instead of producing a plausible guess.
Permission-aware retrieval
Access is enforced at query time against your identity provider. A prompt instruction is not an access control, and we do not treat it as one.
Evaluation before rollout
Golden datasets with subject-expert labels, retrieval metrics separated from generation metrics, and regression gates in CI so quality changes are visible before release.
Budgets and circuit breakers
Token, cost, latency, and step limits per workflow, with hard stops. Agent loops cannot run unbounded.
Full execution tracing
Every retrieval, tool call, and generation is traced, so a bad output can be explained rather than argued about.
Human approval on consequence
Writes to systems of record, financial actions, and customer-facing communication pass a person by design.
Technology we work with
Named to explain the work rather than to imply endorsement. Tool choices follow the requirement, and we work with what you already run wherever that is sensible.
- Claude
- Amazon Bedrock
- Azure OpenAI
- LangGraph
- pgvector
- Qdrant
- OpenSearch hybrid search
- Cross-encoder rerankers
- Ragas
- OpenTelemetry tracing
Getting started
Where a first engagement begins
A four-to-six week assessment: one workflow baselined, an evaluation set built from your real queries, and a working prototype on your own data — with an honest read on whether this should go further.
Discuss an AI initiativeQuestions
Do we need to fine-tune a model, or is retrieval enough?
Usually retrieval first. Fine-tuning changes behaviour and format; retrieval changes what the model knows. If the gap is missing or changing knowledge, fine-tuning is the expensive wrong answer. We make that call explicitly during assessment and write down the reasoning — and where adaptation genuinely is the right tool, that is a separate capability we also deliver.
How do you stop the system inventing answers?
Three things together: generation constrained to retrieved context with citations, a measured abstention threshold so low-coverage queries decline rather than guess, and an evaluation set that specifically includes questions the corpus cannot answer. Groundedness is measured, not asserted.
Will our data be used to train someone else's model?
No. We deploy through enterprise endpoints with training disabled, and where policy requires it we run open-weight models entirely inside your environment. Which of those applies is a decision we make with you at architecture stage.
Who owns what you build?
You do. Code lands in your repositories, workloads run in your accounts, and the evaluation sets and prompts are yours. There is no Seaggle runtime you have to keep licensing to operate your own system.
Discuss an AI initiative
Bring the problem, the current environment, and what better should look like. We will tell you what is realistic before anyone signs anything.