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Problems we solve

Fourteen problems, one architecture each

Every engagement starts from a business problem, not a technology. Each card names the problem, shows how the system works, lists the parts it is built from and links to the outcome the industry has documented.

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What we solve

Every problem we solve, with the system behind it

Read the problem line first. If it sounds like yours, the flow shows what we would build and the architecture link opens the reference design.

Retrieval over every log, with a model that reads them

Failed payments that take two days to explain

A payment fails and the support desk has to say why. Engineers read logs from five or more sources by hand: Java stack traces, cluster logs, gateway traces, each in its own tool and format. One ticket takes about 48 hours.

How the system works

  1. Ticket names the payment
  2. Logs from every source in one index
  3. Hybrid search finds the related lines
  4. Model reads Java errors, cluster logs and gateway traces
  5. Plain-words answer points at the root path
Log storeEmbeddings + BM25RerankerLLMTicket tool

Outcome: About five minutes to the root path instead of 48 hours, in our founder's own deployment for a global bank's payments desk (names withheld).

Architecture
Grounded support agent

Support queues that never shrink

Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.

How the system works

  1. Customer message
  2. Intent & policy check
  3. Retrieve from your docs & orders
  4. Answer or act via tools
  5. Escalate with context
Hybrid RAGTool callingLangGraphZendesk / IntercomLangfuse

Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release

Architecture
Voice agent

Calls missed, callers on hold

Clinics, dealerships and service businesses lose bookings after hours and during peaks. IVR menus frustrate callers and staff repeat the same ten conversations all day.

How the system works

  1. Call arrives
  2. Streaming speech-to-text
  3. Agent reasons & checks calendar / CRM
  4. Streaming text-to-speech
  5. Book, confirm, hand off
Twilio / LiveKitDeepgramElevenLabs / CartesiaOpenAI / GeminiPipecat / Vapi

Outcome: Voice agents replacing IVR resolve a majority of qualified calls automatically (66% at one Parloa customer) and cut wait times by a third or more, at turn latencies under a second. Parloa customer results

Architecture
Document intelligence pipeline

Documents keyed in by hand

Invoices, KYC files, claims and contracts arrive as PDFs and photos. People retype them, errors slip through, and the backlog grows every month-end.

How the system works

  1. Ingest PDF / image / email
  2. Classify document type
  3. Extract to a schema
  4. Validate & score confidence
  5. Auto-post or route to review
Multimodal LLMStructured outputsReducto / LlamaParseReview queueERP / LOS integration

Outcome: Documented deployments classify documents in under a second, turn day-long claims backlogs into minutes, and reach 85% no-touch invoice processing within six months with a seven-month payback. DXC with Claude; Vic.ai

Architecture
Enterprise knowledge assistant

Answers buried in ten systems

Policies live in SharePoint, decisions in email, procedures in Confluence and history in tickets. New staff take months to become useful and experts answer the same questions daily.

How the system works

  1. Connect sources
  2. Chunk with context, embed + index
  3. Hybrid retrieval + rerank
  4. Cited answer with permissions
  5. Feedback into evals
pgvector / QdrantBM25 + embeddingsCohere / Voyage rerankPermission filtersSlack / Teams

Outcome: Contextual chunking with hybrid search and reranking cuts retrieval failures by 67% versus plain vector search, which is the difference between an assistant people trust and one they abandon. Anthropic: Contextual Retrieval

Architecture
WhatsApp commerce agent

Sales conversations stuck on personal phones

Orders, questions and promotions run through WhatsApp with no history, no automation and no way to see what drove revenue.

How the system works

  1. Ad or QR opens chat
  2. AI agent answers from catalogue
  3. Cart & payment in chat
  4. Order synced to store
  5. Campaign & follow-up
WhatsApp Business PlatformShopifyRazorpay / StripeRAG over catalogueTeam inbox

Outcome: 53% of retailers already automate messaging on the WhatsApp Business Platform (Infobip), and the same grounded-agent pattern turns that channel into a store, a support desk and a campaign tool with attribution. Infobip WhatsApp statistics

Architecture
Analytics copilot on a governed semantic layer

Every question needs an analyst

Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.

How the system works

  1. Raw sources
  2. Lakehouse with partitions & quality checks
  3. Semantic layer of metrics
  4. Text-to-SQL agent
  5. Dashboards & alerts
Iceberg / DeltadbtSnowflake / BigQuery / DatabricksCube / dbt Semantic LayerGenie / Cortex Analyst

Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs

Architecture
Orchestrated multi-agent workflow

Swivel-chair work across systems

Onboarding, reconciliation, procurement and approvals require people to read one system and type into another, with rules that live in someone's head.

How the system works

  1. Trigger (email, form, event)
  2. Orchestrator plans steps
  3. Worker agents read & decide
  4. Confirm risky actions
  5. Update systems & log
LangGraph / Agents SDKMCP tool serversCRM / ERP APIsHuman checkpointsTraces & replay

Outcome: CRM-native agents at a regulated vendor deflected 72% of support cases to self-service and saved 7.5 hours per case handled with AI assistance. Smarsh on Salesforce Agentforce

Architecture
Conversational recruiting agent

Hiring that stalls on screening and scheduling

High-volume roles generate thousands of applications; recruiters spend their time on screening questions and calendar tennis instead of on candidates.

How the system works

  1. Candidate applies via chat
  2. Assistant screens against requirements
  3. Books interviews on recruiter calendars
  4. Generates offer & onboarding docs
  5. ATS updated throughout
WhatsApp / SMSStructured extractionCalendar & ATS APIsHybrid candidate searchGuardrails

Outcome: Conversational recruiting assistants report 40,000 hours saved per week at one retailer, 58% faster application flows and 54% lower cost per hire. Paradox customer results

Architecture
Sales agent with CRM tools

Leads that go cold before anyone replies

Inbound leads wait hours, reps spend their time on CRM hygiene, and nobody knows which deals deserve attention this week.

How the system works

  1. Lead arrives
  2. Enrich from web & CRM
  3. Qualify & score
  4. Reply or book meeting
  5. Log to CRM & alert rep
HubSpot / SalesforceWeb enrichmentTool callingWhatsApp / emailGuardrails

Outcome: AI-agent adoption in customer-facing service organisations rose from 39% to 66% between 2025 and 2026; the same grounded-agent pattern applies to first response and qualification in sales. Salesforce research via Agentforce ecosystem guide

Architecture
Model gateway with routing and caching

LLM spend that grows faster than usage

Every team calls model APIs directly, nobody can attribute cost to a feature, and a model upgrade or a traffic spike doubles the bill without warning.

How the system works

  1. One gateway endpoint
  2. Budgets & keys per team
  3. Route: small model first
  4. Prompt & semantic cache
  5. Batch the non-urgent
  6. Cost traced per feature
LiteLLM / PortkeyPrompt cachingRouteLLM-style routerRedisOpenTelemetry

Outcome: Query routing alone cuts cost by more than 85% while retaining 95% of frontier quality; prompt caching bills repeated context at a tenth of the price and batch APIs halve everything non-urgent. RouteLLM (ICLR 2025)

Architecture
Eval-gated delivery pipeline

AI features that quietly get worse

A prompt tweak fixes one case and breaks three others. A provider upgrades a model and answers change tone or accuracy. Nobody notices until customers do.

How the system works

  1. Golden set from real traffic
  2. Offline evals in CI
  3. Release gate on thresholds
  4. Traces sampled online
  5. Failures fed back to the set
Langfuse / LangSmithPromptfoo / DeepEvalLLM-as-judge, calibratedGitHub ActionsGrafana

Outcome: Regression evals target near-100% pass on known cases before every release, with judges calibrated against human reviewers and gates that block a drop of more than a few points. Anthropic: Demystifying evals for AI agents

Architecture
Computer-use agent with approval gates

Software that has no API

A legacy ERP, a partner portal or a government site sits in the middle of the process, and the only way in is a screen and a keyboard.

How the system works

  1. Task & policy
  2. Screenshot
  3. Vision model decides
  4. Click / type in a sandbox
  5. Verify state
  6. Approve irreversible steps
Claude computer use / OpenAI CUASandboxed browser or VMbrowser-use / PlaywrightApproval queueScreenshot traces

Outcome: Top computer-use agents now exceed the 72% human baseline on OSWorld's real desktop tasks; kept to short, verified steps they remove the last manual hop in a process. OSWorld benchmark

Architecture
Visual document retrieval

Answers locked in scans, slides and drawings

Engineering drawings, slide decks, scanned contracts and table-heavy PDFs lose their meaning when flattened to text, so search returns nothing useful.

How the system works

  1. Rasterise pages
  2. Patch embeddings (ColPali)
  3. Multi-vector index
  4. Retrieve pages, not chunks
  5. Vision model answers with citations
ColPali / ColQwenVespa / QdrantGemini / GPT / Claude visionLanceDBGolden set

Outcome: Late-interaction retrieval over page images scored 81.3 nDCG@5 on the ViDoRe benchmark against roughly 65–75 for OCR pipelines, with no OCR stack to maintain. ColPali (ICLR 2025)

Architecture

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