CAPABILITYOVERPACKAGE.
We don't sell retainers or generic packages. We pick the right primitives for what you're actually building — and put them together so they work in production, not just demos.
under one team
to production
on handoff
Autonomous Agents
Systems that don't wait to be told what to do next. We design agent architectures that perceive state, reason over context, select tools, and complete multi-step tasks — with the right guardrails so they don't go off-script in production.
// What's included
- Agent topology design — single, multi, or hierarchical
- Tool and function definition with typed schemas
- Long-term and short-term memory layer design
- Failure detection, retry logic, and fallback paths
- Human-in-the-loop checkpoints where needed
- Evaluation harness with task-completion benchmarks
- Production deployment with observability hooks
// Deliverables
Agent graph, tool registry, memory schema — signed off before build starts.
Tested, deployed, and monitored agent with eval suite showing task completion rate.
How to observe, debug, retrain, and extend the agent after we hand off.
94%
Autonomous research agent for a Series B fintech — replaced a manual analyst workflow running 3 hours/day, handling market data ingestion, summarisation, and flagging.
Orchestration Pipelines
When one agent isn't enough. We build multi-agent systems where specialised agents coordinate, delegate, and check each other's work — with state machines, routing logic, and graceful degradation baked in from day one.
// What's included
- Multi-agent topology with role separation
- Task routing, priority queues, and load balancing
- Inter-agent communication protocol design
- State persistence across long-running workflows
- Timeout, retry, and circuit-breaker patterns
- Tracing and logging at each node in the graph
- Integration with your existing APIs and data sources
// Deliverables
Visual + code representation of agent roles, routing rules, and state transitions.
Running system with staging environment, integration tests, and rollback plan.
Per-agent latency, error rates, and queue depth — visible in one place.
4× faster
Document processing pipelines where one agent extracts, one validates, one routes, and one writes to your database — with human escalation for edge cases.
Generative AI & RAG
We build GenAI products that work on your data — not the model's training data. RAG pipelines, document Q&A, summarisation systems, content generation engines. Every output grounded in sources you control and can verify.
// What's included
- Document ingestion pipeline — PDF, HTML, databases, APIs
- Chunking strategy tailored to your content type
- Embedding model selection and vector store setup
- Query routing, hybrid retrieval, re-ranking
- Source citation and hallucination reduction layers
- Streaming UI with real-time token delivery
- Feedback loop for continuous quality improvement
// Deliverables
Ingestion, embedding, indexing, and retrieval — benchmarked against your real queries.
Prompt templates, system instructions, and output formatting — tuned for your use case.
Faithfulness, relevance, and answer quality scores. Not vibes — actual metrics.
70% ↓
Internal knowledge base Q&A for a 200-person ops team — reduced 'where do I find X' Slack messages by 70%, replacing 4 hours of manual search per day.
Semantic Search
Keyword search returns results that contain your words. Semantic search returns results that match your intent. We build vector-native and hybrid search systems that understand what the user actually meant — and surface it fast.
// What's included
- Embedding model selection and fine-tuning for your domain
- Vector store setup — managed or self-hosted
- Hybrid BM25 + vector search with configurable weights
- Re-ranking layer for final result ordering
- Filtering, faceting, and metadata-aware retrieval
- Latency benchmarking and ANN index tuning
- A/B test framework for search quality comparison
// Deliverables
End-to-end retrieval system: ingest → embed → index → query → rerank.
NDCG, MRR, and recall scores against your labelled query set.
Search API your frontend or downstream systems can call directly.
12× faster
Rebuilt product catalogue search for an e-commerce client — replaced ElasticSearch keyword search with hybrid retrieval. Relevant results in top-3 went from 41% to 89%.
Model Development
When a general-purpose LLM isn't accurate enough, fast enough, or cheap enough for your domain. We handle the full model lifecycle — from dataset curation to fine-tuning to deployment — with eval frameworks that tell you if the model is actually better.
// What's included
- Dataset audit, curation, and labelling pipeline design
- Base model selection and cost-vs-accuracy tradeoff analysis
- LoRA / QLoRA fine-tuning with hyperparameter sweeps
- Evaluation suite: domain-specific benchmarks + human eval
- RLHF or DPO alignment where quality requires it
- Model quantisation for inference cost reduction
- Deployment via API with versioning and rollback
// Deliverables
Cleaned, formatted, and versioned dataset with labelling guidelines.
Adapter weights or full model — deployable via HuggingFace, vLLM, or your infra.
Before/after comparison across your task metrics. Numbers, not claims.
// When to fine-tune vs. when to prompt
| Scenario | Prompt engineering | Fine-tuning |
|---|---|---|
| General task, any domain | ✓ | – |
| Consistent output format | ✓ | ✓ |
| Domain-specific terminology | – | ✓ |
| High volume, low latency | – | ✓ |
| Sensitive / private data | – | ✓ |
94%
Domain-specific classifier for a legal tech platform — Llama 3 fine-tuned on 12k labelled contracts. Extraction accuracy went from 61% (GPT-4o prompting) to 94% (fine-tuned small model) at 80% lower cost per call.
Full-Stack Delivery
AI features don't live in isolation. They need real backends, real frontends, real auth, and real infrastructure. We build the whole product — not just the model layer — so you're not left stitching things together yourself.
// What's included
- Backend API design and implementation (FastAPI / Node)
- Frontend application with streaming AI UI patterns
- Auth, billing, and user management
- Background job queues for async AI workloads
- Database schema design and migration strategy
- CI/CD pipeline with test coverage gates
- Infrastructure as code with monitoring and alerting
// Deliverables
Running in production with SSL, monitoring, and auto-scaling configured.
Clean repo, typed, tested, and wired to your deploy pipeline. Ready to hire into.
Terraform or CDK infra with runbook covering scaling, backups, and incident response.
8–12w
End-to-end AI SaaS product: auth, dashboard, AI backend, streaming chat, user billing, admin panel, and deployment pipeline — 8–12 week build.
// Engagement models
Three ways
to work together.
Depending on where you are and how much you want us involved.
Scoped Build
A defined problem, a fixed scope, a clear deliverable. Best for teams that know what they want built and need it done right.
- Fixed timeline and deliverables
- Weekly check-ins during build
- Full handoff with docs
- 30-day post-launch support
Embedded Sprint
We join your team for 4–12 weeks, own a specific AI capability end-to-end, and transfer knowledge throughout. Best for teams building an internal AI function.
- Full integration with your tools and repo
- Daily standups + async Slack
- Knowledge transfer throughout
- Option to extend or hire from us
Advisory
You have engineers. You need someone who's built this before to review architecture, unblock decisions, and accelerate your team.
- Architecture review and sign-off
- Monthly 90-min strategy session
- Async Q&A on Slack or email
- Code review on critical paths
// Common questions