We integrate large language models directly into your existing products and workflows, turning raw AI capability into reliable business features. From API integration and custom prompt engineering to evaluation frameworks, we ensure the models we deploy behave predictably and stay within scope.
Through structured testing and continuous monitoring, we build LLM-powered features that deliver consistent value at production scale—not just in a controlled sandbox where edge cases never appear.
Most LLM implementations fail in two ways: they work perfectly in demos but fail silently in production, or they produce outputs that are unpredictable enough that teams stop trusting them. We've learned that integration isn't about deploying the latest model—it's about building evaluation frameworks, monitoring systems, and feedback loops that catch degradation before users do.
Our approach starts with rigorous assessment of your use case and data. We build working prototypes that include not just the model integration, but the fallback logic, prompt engineering, and cost-optimisation that makes deployment sustainable. We establish clear success metrics around accuracy, latency, and cost—then build monitoring that tracks these metrics continuously. If an LLM isn't the right answer for your workflow, we tell you before you've invested in integration that won't deliver value.
The LLM integration consumes long documents, transcripts, and conversations—extracting key points and generating executive summaries that respect your domain-specific context and terminology. Deployed with evaluation metrics that track summary accuracy and completeness.
The LLM integration builds a knowledge assistant that fields questions about your product, policies, or operational procedures. It retrieves relevant context from your documentation and generates answers in real time, with fallback to human escalation when confidence is low.
The LLM integration analyses your codebase and generates documentation, function descriptions, and inline comments that keep pace with implementation changes. Evaluation frameworks verify that generated documentation matches code intent.
The LLM integration reads unstructured text—emails, documents, support tickets—and extracts structured data aligned with your schema. It handles edge cases, flags anomalies, and maintains audit trails of extraction decisions.
The LLM integration translates, localises, and adapts content across languages while preserving brand voice and domain-specific terminology. Evaluation includes both linguistic accuracy and cultural appropriateness.
Asset Management & Investment Funds
Private Equity & Venture Capital
Banking & Financial Services
Audit & Assurance Services
Legal & Compliance
Professional Services & Consulting
Insurance & Reinsurance
Corporate Governance & Board Services
We integrated a custom LLM-powered research assistant into their workflow, connecting model outputs to their proprietary research database and trading systems. The implementation included evaluation frameworks that monitored research quality and fallback logic that prevented low-confidence outputs from reaching traders.
We built an LLM-powered compliance documentation assistant that consumed audit briefs and generated compliance narratives aligned with regulatory frameworks. The system included domain-specific prompt engineering and continuous evaluation against audit standards.
We integrated LLM-powered transaction categorisation into their compliance platform, enabling real-time classification of payments against regulatory frameworks. The system was designed with cost controls and fallback logic to ensure reliability under high transaction volumes.
We deployed a customer support chatbot powered by custom LLM integration, connected to their transaction history and policy documentation. The system included confidence thresholds that escalated uncertain queries to human support specialists.
Every LLM integration is unique. Your domain has specific terminology, your workflows have particular constraints, and your definition of "good output" doesn't match anyone else's. Building integration that actually works requires understanding your specific context—not applying generic prompt templates or assuming that a larger model will solve reliability issues.
What we bring is production experience from dozens of LLM deployments, discipline around evaluation and monitoring, and the honesty to recommend against model integration when rule-based alternatives would serve your users better.
We begin by understanding your use case in detail—not just the desired output, but the data it will draw on, the users who will interact with it, and what failure looks like. We review a sample of real inputs to understand the variation, edge cases, and domain-specific language the model will encounter. Many LLM integration problems are actually data problems or workflow design problems that a model alone won't solve; this phase surfaces those realities early.
We also establish what success looks like in measurable terms: accuracy thresholds, latency requirements, cost constraints, and user adoption expectations. These become the criteria against which every subsequent decision is evaluated.
Outcome: Use case specification, data assessment, success criteria definition, risk and feasibility summary
We evaluate candidate models against your specific use case—testing on representative samples of your actual data, not synthetic benchmarks. Model selection involves trade-offs between accuracy, latency, cost, and data privacy requirements; what performs best on a public leaderboard is rarely the right choice for a regulated financial services workflow.
Where appropriate, we evaluate open-source and self-hosted options alongside commercial APIs. For use cases with sensitive data, on-premise or private cloud deployment may be the right answer regardless of which model performs best in isolation.
Outcome: Model evaluation report, performance benchmarks on your data, recommended model and deployment approach
We design the prompt architecture for your specific use case—including system instructions, few-shot examples, output format constraints, and the chain-of-thought structure needed to produce consistent results. Prompt engineering is iterative; we test against a wide sample of real inputs and refine until performance meets the success criteria established in discovery.
We also build the evaluation harness used to score prompt performance systematically, so future changes can be validated quickly rather than tested manually. This becomes part of the handoff—your team inherits a tool that tells you whether a prompt change is an improvement.
Outcome: Production prompt architecture, evaluation harness, performance benchmarks, prompt documentation
We design the full integration—how the model connects to your existing systems, how inputs are pre-processed before reaching the model, how outputs are validated and post-processed before reaching users, and where human review is inserted for low-confidence or high-stakes outputs. We also design the fallback logic: what happens when the model is unavailable, slow, or returns output below confidence thresholds.
This phase produces the technical specification that guides development. We make explicit decisions about API structure, caching strategy, rate limiting, cost controls, and the data flow between your existing infrastructure and the model layer.
Outcome: Integration architecture specification, data flow design, fallback and error handling logic, API design
Before deployment, we build the evaluation infrastructure that will track model performance in production. This includes automated test suites that run against a labelled dataset of known inputs and expected outputs, as well as production monitoring that tracks real-world accuracy, latency, and cost against the baseline established during development.
We set alert thresholds for the metrics that matter most to your use case—whether that's accuracy dropping below a defined threshold, latency spiking under load, or cost-per-request exceeding budget constraints. Monitoring gives your team visibility without requiring manual review of every model output.
Outcome: Automated evaluation suite, production monitoring dashboard, alert configuration, baseline metrics documentation
Models degrade. Input distributions shift, new edge cases emerge, and the business context the model needs to understand evolves over time. We establish a regular review cadence—examining production metrics, reviewing escalated or flagged outputs, and identifying patterns that indicate the model needs attention.
Updates are deployed through the same evaluation harness built during development, so changes are validated against the full test suite before reaching production. We work with your team to build the internal capability to manage ongoing optimisation, so the integration remains performant without depending on external support indefinitely.
Outcome: Optimisation roadmap, regular performance reviews, updated evaluation benchmarks, team capability handoff
We offer flexible engagement options to match your integration needs, timeline, and budget. Choose the model that fits—or combine them as your LLM programme evolves.
The primary engagement model for ongoing LLM integration development and optimisation. Provides dedicated team capacity, predictable budgeting, and priority scheduling. Works best for continuous integration work, iterative improvements based on production feedback, and long-term partnerships where deep product knowledge drives efficiency.
Available exclusively for clearly scoped PoC engagements with defined success criteria and evaluation frameworks. Provides cost certainty while validating whether LLM integration will deliver measurable value for your specific use case. Concludes with documented results, performance metrics, and implementation roadmap.
Best suited for short-term integration acceleration, specific expertise needs, or variable scope projects. Billing is based on actual hours worked with complete visibility into team composition and time allocation. Maximum flexibility to scale capacity as integration needs evolve.
A senior LLM engineer embeds within your team, working on model integration, evaluation, and optimisation as a direct report to your technical leadership. This model works well for ongoing LLM initiatives, rapid experimentation, or when you need hands-on guidance on model selection and integration decisions.
Frequently Asked
Questions
