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For chat-powered products
Chatbots & AI Assistants
Customer-support bots, in-product copilots, RAG-powered Q&A, document-grounded assistants. Citations on every reply, abstention when confidence is low.
LLM apps, agentic workflows, computer vision, fine-tuned models, and AI-driven automation — engineered with evaluation gates, cost caps, and graceful fallbacks. Demos that survive contact with real users.
Categories that cover the full range of AI engagements in 2026 — from a grounded LLM copilot to an automated ML pipeline running every night at 2 AM.
Customer-support bots, in-product copilots, RAG-powered Q&A, document-grounded assistants. Citations on every reply, abstention when confidence is low.
Multi-step agents that plan, call tools, evaluate, and self-correct. Cost-capped, eval-gated, with human approval at the steps that matter.
Image classification, object detection, OCR, document parsing. On-device or in the cloud — picked per use case, not by fashion.
LoRA, QLoRA, full fine-tunes on Hugging Face or vendor APIs. Eval suites built first, training driven by what the eval can see.
Data ingestion, training, evaluation, deployment, monitoring — all reproducible, all versioned, all cheap to roll back.
AI-classified routing, smart summaries, generated reports, agent-driven ops. Wired into the tools your team already uses.
The pain points that sink most AI builds, and the way we solve each one. No magic — just engineering discipline applied to a probabilistic stack.
The model confidently invents an answer. One bad reply and your support team spends a week walking it back across every channel.
Retrieval-augmented generation, source-attached answers, abstention when confidence is low. Users see where every claim comes from.
A free-tier prompt loop, an unbounded retry, a prompt injection — and the bill is four-figures-per-day before anyone looks at the logs.
Per-user, per-feature, per-day cost caps with hard cutoffs. Smaller models for cheap paths, big models only where it earns the spend.
A polished demo on hand-picked queries. The same system on real user input fails on the long tail no one tested for.
Versioned eval suites, golden datasets, regression detection on every model swap. We promote builds the eval can prove are better.
Years of free-text fields, mis-labelled tickets, dirty PDFs. The model is only as smart as the data infrastructure that feeds it.
Ingestion, cleaning, labelling, versioning. Reproducible, auditable, and cheap to roll back when a source schema shifts.
Stock model, no custom routing or eval suite, vendor walks after the demo. Hallucinations and cost spikes become your problem.
Agentic workflow, eval suite, and integrations your project asks for — and the team committed through drift, swaps, and edge cases.
Anyone can ship a demo. We ship the version that survives a million prompts, two vendor outages, and a procurement review.
Honest fixes for common challenges. Same playbook on every engagement, calibrated to the size of your build.
Eval-gated rollouts, cost caps, vendor-neutral abstractions, privacy-first data handling, observability, and an architecture that survives the next model upgrade.
Versioned eval suites, golden datasets, regression detection. We promote a build only after the eval can prove it’s better.
Per-user, per-feature, per-day spend limits with hard cutoffs. Smaller models on cheap paths; the big ones only when they earn the bill.
Routing layer across Claude, OpenAI, Gemini, Mistral, and open-weights. Vendor-neutral abstractions; swap providers without touching product code.
PII redaction, prompt logging with retention policies, on-prem inference where compliance demands it. SOC-2 friendly architecture from day one.
Every prompt traced, every chain stepped through, every token counted. When something drifts, you see it before customers do.
Agentic workflows, eval suites, and integrations your project asks for — with the team committed past drift, swaps, and edge cases.
Anyone can wire an LLM into a form. We engineer the layer underneath — evals, cost caps, routing, fallbacks — that keeps the system honest after thousands of real prompts.
We design the evaluation suite before the agent — what does success look like, on what data, at what threshold.
Per-user, per-feature, per-day. Hard cutoffs prevent the four-figure-bill surprise.
Routing layer across Claude, OpenAI, Gemini, Mistral, open-weights. Swap providers via config, not rewrite.
When the model fails, the product still works. Deterministic rules, smaller models, human-in-the-loop where it matters.
Plan, retrieve, call tool, verify, ship — every step cost-capped, eval-gated, and recoverable.
Same workflow on every engagement, calibrated to project size. Discover, design, engineer, optimise, launch.
Use case, success metric, data shape, compliance constraints. We map what the model needs to do, what data it can see, and what failure looks like.
Eval suite, prompt architecture, retrieval strategy, fallback paths. The system is designed for what happens when the model is wrong.
Routing layer, cost caps, observability, guardrails. Multi-model abstractions; vendor swaps are config changes, not rewrites.
Eval-driven prompt tuning, model selection per path, latency budgets, cost-per-task targets. Rollouts gated by eval delta, not by sprint date.
Production rollout with feature flags, eval monitoring, cost tracking, and a path to fine-tune or swap models as the SOTA shifts.
We don’t do “all industries.” These are the verticals where we’ve done enough AI work to bring real domain context into the kickoff call.
In-product copilots, RAG over user data, AI-driven onboarding & churn signals.
Clinical document intelligence, patient triage chat, HIPAA-aware data flows.
KYC automation, transaction categorisation, fraud signal pipelines, compliance Q&A.
Route optimisation, demand forecasting, computer-vision package scanning.
Tutor agents, curriculum generators, AI-graded assessments with citations.
Foundation-model wrappers, eval-driven product development, multi-tenant agents.
Listing classification, virtual staging, AI lead scoring, document parsing.
Product recommendations, AI search, customer-service copilots, review summarisation.
AI concierge, multi-locale guest chat, dynamic pricing, sentiment monitoring.
A glance at recent AI engagements. The full story — problem, solution, tech, timeline — lives on each case-study page.
What we do in this space
We build AI-powered automation systems that integrate directly into your existing workflows — intelligent chatbots, document processing, workflow orchestration, and LLM-backed decision tools. Our current automation projects are underway and we are actively taking on new engagements. Talk to us about what you want to automate.
Tell us where you’re going. We’ll come back with the senior engineer + designer who’d lead the engagement.