AI capability, without building an AI department.
Practical tools. Tangible results. Real people
Silky Insights is a specialist AI implementation team that helps organisations add practical AI functionality to existing products, workflows, and data environments.
We help you decide what to build, prepare the data needed to build it, and build the AI capability itself.
AI projects get stuck between ambition and implementation.
The use case is unclear
The data is not ready
The team is not resourced to build it
Many teams can see the potential of AI, but do not have the internal capability, time, or confidence to turn it into reliable software.
The use case seems promising, but the data is messy. The prototype looks impressive, but does not survive real workflows. Internal teams are busy, hiring is slow, and it is hard to know what is valuable, feasible, or safe to build first.
That is where Silky comes in.
Three ways we help
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We design, build, and integrate AI functionality into existing products, platforms, internal tools, and customer experiences.
This could include search, recommendations, summarisation, document generation, classification, structured extraction, workflow automation, chat interfaces, or user-facing AI features.
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AI is only as useful as the information underneath it.
We clean, structure, connect, and organise documents, reports, databases, and knowledge sources so they can support reliable AI.
This can include document ingestion, structured extraction, foundational databases, data pipelines, metadata, vector search, APIs, monitoring, and reporting reliability.
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Not every AI idea is worth building.
We help you identify where AI can create real value, assess feasibility and risk, and define a practical roadmap. The output is not a strategy deck for its own sake. It is a clear path to action.
Working AI systems, not just advice
We are strongest when we can point to systems that are already in use. Our work has helped organisations turn complex information into structured, searchable, and useful software. Across energy, media, HR, and compliance, the pattern is similar: valuable knowledge is trapped in documents, workflows, databases, or archives, and teams need a reliable way to turn it into action.
AI-Ready Data Foundations, AI Product Implementation, Document Intelligence
Enerlytica
Enerlytica works with dense, high-value energy market information, including reports, PDFs, data tables, and recurring data flows. The opportunity was not simply to “add AI”, but to create a more reliable information layer that could support faster research, better reporting, and future AI-enabled products.
Silky helped convert complex documents and data sources into structured, searchable, and more usable information. This included work across document ingestion, table extraction, foundational database design, search capability, API-connected workflows, and improvements to the reliability of data processes.
The result was a stronger foundation for AI: one where valuable historical reports, market commentary, tabular information, and recurring data could be accessed and reused more effectively.
Product Implementation, Embedded AI, Workflow Copilots, Document Generation
Humaneer
Humaneer needed practical AI capability layered into an existing HR and compliance product. The challenge was to turn rich workplace information, including transcripts, context, policies, and user inputs, into useful outputs that could support real HR and compliance workflows.
Silky supported the development of AI-powered layers that use meeting transcripts, knowledge retrieval, contextual reasoning, document generation, and compliance guidance. Rather than building AI as a novelty feature, the work focused on helping users move from conversation and context to practical next steps.
This is the type of AI implementation where trust, traceability, and workflow fit matter. The system needed to support human judgement, not replace it.
Archive Discovery, User-facing AI Features, Product Integration, Semantic Search
BusinessDesk
BusinessDesk had a large and growing archive of business journalism, market information, company coverage, journalist profiles, and related content. The opportunity was to improve how readers discovered relevant information and how the platform connected users with the right content at the right time.
Silky built AI-powered search and recommendation capability that went beyond simple keyword matching. The system was designed to understand user queries and article context, helping surface more relevant articles, related content, and recommendations across the archive.
This work showed how AI can be integrated into an existing digital product to improve discovery, engagement, and user experience.
Why Silky
Senior AI capability without the hiring risk
Hiring an internal AI team can make sense once AI becomes core to your business.
But hiring before you know what you need can be slow, expensive, and risky. Silky helps you prove the use case, define the architecture, build the first working system, and create a clearer basis for future investment.
You work directly with the people designing and building the solution. No large consulting team. No endless strategy programme. No handover gap between advice and implementation.
A lower-risk way to start
The usual path
Hire before the role is clear
Run broad AI workshops
Build demos that do not fit real workflows
Leave internal teams to figure out AI patterns alone
Commit budget before value is proven
The Silky path
Start with the workflow, product, or decision that matters
Assess value, feasibility, data readiness, and risk
Build a useful first version with real data
Integrate it into the existing environment
Scale what works, once value is clear
From uncertainty to working capability
We usually start small, prove value quickly, and scale what works.
01. Understand the real problem
We start with the workflow, product, decision, or information problem you are trying to improve.
02. Identify the right AI opportunity
We assess value, feasibility, data readiness, risks, and the fastest path to a useful first version.
03. Build with real data and real users
We create practical prototypes and MVPs that can be tested in the environment they are meant to serve.
05. Improve and scale
We harden what works with evaluation, monitoring, feedback loops, documentation, and production support.
04. Integrate into the workflow
We design AI to fit into existing products, systems, permissions, and user behaviour.
Let’s work out what is worth building.
Whether you are trying to add AI into an existing product, make your data usable for AI, or understand where AI should fit in your business, we can help you find a practical next step.