Contract review, specification comparison, tender assessment, and policy conflict detection across full documents rather than fragments. Output is structured for downstream systems and carries references back to the passages that produced it.

A single well-built server makes a system available to every model and application that speaks the protocol. We handle schema design, authentication, permission scoping, and logging so the integration is genuinely reusable.

Codebase comprehension, migration assistance, test generation, and review support using agentic coding tooling. We establish guardrails and review conventions so productivity gains do not arrive alongside a drop in code quality.

Assistants that answer only from approved sources and show exactly where each claim came from. Refusal behaviour when sources are insufficient is designed deliberately rather than left to chance.

Older systems with no API can sometimes be driven through their interface. We treat this as a last resort with tight sandboxing, action logging, and human confirmation on anything consequential.

Classification, extraction, and summarisation across large historical collections submitted in batches at reduced cost. Suits any workload measured in documents processed rather than in response times.

Ordering, delimiting, and labelling material inside a large prompt measurably affects accuracy. We structure documents explicitly, place instructions where they hold attention, and test what happens as inputs approach the window limit.

Local versus remote transport, tool granularity, resource exposure, and error semantics all shape usability. A server designed around how models actually call tools performs noticeably better than one mirroring an existing internal API.

Clear definitions with narrow types, plus recognising when multiple independent calls can run concurrently rather than sequentially. Parallel execution often halves perceived latency on multi-step tasks at no additional cost.

When the same lengthy material appears across many requests, caching that prefix reduces cost substantially. Structuring prompts so the stable portion sits first is what makes the saving available at all.

Extended reasoning improves difficult tasks and wastes money on simple ones. We measure the accuracy gain per task type, then configure accordingly instead of enabling maximum deliberation everywhere by default.

A pipeline commonly uses the smallest tier for triage, a middle tier for the main pass, and the largest only for cases flagged as ambiguous. Designing that routing early keeps unit economics reasonable at volume.

One document class, a labelled sample, and a measured accuracy figure with cost per document. The output is a decision-grade number rather than a demonstration, and the labelled set remains yours afterwards.

One or more servers exposing internal systems through the protocol, with authentication, permission scoping, logging, and documentation. Built as reusable infrastructure rather than as a dependency of a single application.

Configuration, guardrails, review conventions, and practical sessions with your development team. Measured on merged output quality and cycle time rather than on how enthusiastic anyone felt afterwards.

Moving an implementation into your existing cloud for contracting, regional, or network reasons. Covers configuration, capacity planning, behaviour verification against the incumbent, and staged cutover with rollback available.

One specialist working inside your sprints on continuous model layer work. Your engineers absorb context engineering and caching practice through daily contact rather than through documentation nobody reads.

Covering evaluation reruns when new model versions arrive, prompt maintenance, caching review, spend reporting, and incident response. Sized to genuine need rather than bundled into a build contract.

Subject specialists label a representative sample including the awkward cases. Without this, every later accuracy claim is opinion, and disagreements about quality become impossible to settle objectively.

Cost scales with input length, so a pipeline reading whole documents behaves very differently from one reading extracts. We project spend across expected volume and document size distribution before committing to an architecture.

Stable material placed at the front of every prompt becomes cacheable. Recognising this during design rather than after launch is often the difference between viable and unaffordable at volume.

Where output must be checked, we build interfaces that jump straight to the cited passage. Review speed determines whether human oversight survives contact with a real workload.

Throughput limits, request queuing, and graceful degradation under load. Batch submission handles anything deferrable, keeping synchronous capacity available for work that genuinely cannot wait.

Once a pipeline works on a capable tier, we test whether a smaller one performs adequately on part of the workload. This routinely reduces cost with no measurable accuracy change, and it is rarely attempted without prompting.

Comprehension of undocumented systems, migration planning, dependency analysis, and test coverage generation. Long context suits this particularly well, since understanding a module usually requires seeing much more than the module.

Bills of lading, charter agreements, inspection reports, and customs paperwork arriving in inconsistent formats across many parties. Discrepancy detection between related documents is where most of the value sits.

Technical reports, permit conditions, maintenance records, and safety documentation. Regulatory obligations require traceability, which makes citation-backed output a requirement rather than a nice addition.

Specification compliance checking, tender assessment, qualification documentation, and configuration control. Handling constraints here shape architecture heavily, and deployment often has to sit inside an approved cloud region.

Assessment against published criteria, evidence review, report drafting, and consistency checking across assessors. Defensibility of every judgement is the product, so unreferenced output has no value at all.

Drawing schedule interrogation, planning condition tracking, specification coordination, and consultant response review. Small teams handling large document sets get disproportionate benefit from well-scoped automation.

Direct questions about document types, volumes, accuracy tolerance, existing systems, and cloud arrangements, followed by a candid feasibility view. Where the work looks unattractive we say so at the end of that call.

A representative extract reveals formatting inconsistency, extraction difficulty, and the realistic accuracy ceiling. We return observations on data readiness alongside an estimated cost per document at your expected volume.

Narrow scope, agreed success criteria, and a measured result. You finish with an accuracy figure, a unit cost, a labelled dataset you own, and a clear basis for deciding either way.

Profiles arrive with relevant long-context and integration experience. Your team assesses them against your own bar, declines at no cost, and matching continues until the technical fit is right.

Credentials, repository access, document samples, evaluation environment, and sprint planning handled immediately. Meaningful work lands in the first fortnight rather than after one spent reading.

Support arrangements start light and expand only when usage justifies it. Model transitions, caching review, and spend reporting are the components most clients take up first.